examples of graphical representation

Graphical Representation of Data

Graphical representation of data is an attractive method of showcasing numerical data that help in analyzing and representing quantitative data visually. A graph is a kind of a chart where data are plotted as variables across the coordinate. It became easy to analyze the extent of change of one variable based on the change of other variables. Graphical representation of data is done through different mediums such as lines, plots, diagrams, etc. Let us learn more about this interesting concept of graphical representation of data, the different types, and solve a few examples.

Definition of Graphical Representation of Data

A graphical representation is a visual representation of data statistics-based results using graphs, plots, and charts. This kind of representation is more effective in understanding and comparing data than seen in a tabular form. Graphical representation helps to qualify, sort, and present data in a method that is simple to understand for a larger audience. Graphs enable in studying the cause and effect relationship between two variables through both time series and frequency distribution. The data that is obtained from different surveying is infused into a graphical representation by the use of some symbols, such as lines on a line graph, bars on a bar chart, or slices of a pie chart. This visual representation helps in clarity, comparison, and understanding of numerical data.

Representation of Data

The word data is from the Latin word Datum, which means something given. The numerical figures collected through a survey are called data and can be represented in two forms - tabular form and visual form through graphs. Once the data is collected through constant observations, it is arranged, summarized, and classified to finally represented in the form of a graph. There are two kinds of data - quantitative and qualitative. Quantitative data is more structured, continuous, and discrete with statistical data whereas qualitative is unstructured where the data cannot be analyzed.

Principles of Graphical Representation of Data

The principles of graphical representation are algebraic. In a graph, there are two lines known as Axis or Coordinate axis. These are the X-axis and Y-axis. The horizontal axis is the X-axis and the vertical axis is the Y-axis. They are perpendicular to each other and intersect at O or point of Origin. On the right side of the Origin, the Xaxis has a positive value and on the left side, it has a negative value. In the same way, the upper side of the Origin Y-axis has a positive value where the down one is with a negative value. When -axis and y-axis intersect each other at the origin it divides the plane into four parts which are called Quadrant I, Quadrant II, Quadrant III, Quadrant IV. This form of representation is seen in a frequency distribution that is represented in four methods, namely Histogram, Smoothed frequency graph, Pie diagram or Pie chart, Cumulative or ogive frequency graph, and Frequency Polygon.

Advantages and Disadvantages of Graphical Representation of Data

Listed below are some advantages and disadvantages of using a graphical representation of data:

• It improves the way of analyzing and learning as the graphical representation makes the data easy to understand.
• It can be used in almost all fields from mathematics to physics to psychology and so on.
• It is easy to understand for its visual impacts.
• It shows the whole and huge data in an instance.
• It is mainly used in statistics to determine the mean, median, and mode for different data

The main disadvantage of graphical representation of data is that it takes a lot of effort as well as resources to find the most appropriate data and then represent it graphically.

Rules of Graphical Representation of Data

While presenting data graphically, there are certain rules that need to be followed. They are listed below:

• Suitable Title: The title of the graph should be appropriate that indicate the subject of the presentation.
• Measurement Unit: The measurement unit in the graph should be mentioned.
• Proper Scale: A proper scale needs to be chosen to represent the data accurately.
• Index: For better understanding, index the appropriate colors, shades, lines, designs in the graphs.
• Data Sources: Data should be included wherever it is necessary at the bottom of the graph.
• Simple: The construction of a graph should be easily understood.
• Neat: The graph should be visually neat in terms of size and font to read the data accurately.

Uses of Graphical Representation of Data

The main use of a graphical representation of data is understanding and identifying the trends and patterns of the data. It helps in analyzing large quantities, comparing two or more data, making predictions, and building a firm decision. The visual display of data also helps in avoiding confusion and overlapping of any information. Graphs like line graphs and bar graphs, display two or more data clearly for easy comparison. This is important in communicating our findings to others and our understanding and analysis of the data.

Types of Graphical Representation of Data

Data is represented in different types of graphs such as plots, pies, diagrams, etc. They are as follows,

Related Topics

Listed below are a few interesting topics that are related to the graphical representation of data, take a look.

• x and y graph
• Frequency Polygon
• Cumulative Frequency

Examples on Graphical Representation of Data

Example 1 : A pie chart is divided into 3 parts with the angles measuring as 2x, 8x, and 10x respectively. Find the value of x in degrees.

We know, the sum of all angles in a pie chart would give 360º as result. ⇒ 2x + 8x + 10x = 360º ⇒ 20 x = 360º ⇒ x = 360º/20 ⇒ x = 18º Therefore, the value of x is 18º.

Example 2: Ben is trying to read the plot given below. His teacher has given him stem and leaf plot worksheets. Can you help him answer the questions? i) What is the mode of the plot? ii) What is the mean of the plot? iii) Find the range.

Solution: i) Mode is the number that appears often in the data. Leaf 4 occurs twice on the plot against stem 5.

Hence, mode = 54

ii) The sum of all data values is 12 + 14 + 21 + 25 + 28 + 32 + 34 + 36 + 50 + 53 + 54 + 54 + 62 + 65 + 67 + 83 + 88 + 89 + 91 = 958

To find the mean, we have to divide the sum by the total number of values.

Mean = Sum of all data values ÷ 19 = 958 ÷ 19 = 50.42

iii) Range = the highest value - the lowest value = 91 - 12 = 79

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Practice Questions on Graphical Representation of Data

Faqs on graphical representation of data, what is graphical representation.

Graphical representation is a form of visually displaying data through various methods like graphs, diagrams, charts, and plots. It helps in sorting, visualizing, and presenting data in a clear manner through different types of graphs. Statistics mainly use graphical representation to show data.

What are the Different Types of Graphical Representation?

The different types of graphical representation of data are:

• Stem and leaf plot
• Scatter diagrams
• Frequency Distribution

Is the Graphical Representation of Numerical Data?

Yes, these graphical representations are numerical data that has been accumulated through various surveys and observations. The method of presenting these numerical data is called a chart. There are different kinds of charts such as a pie chart, bar graph, line graph, etc, that help in clearly showcasing the data.

What is the Use of Graphical Representation of Data?

Graphical representation of data is useful in clarifying, interpreting, and analyzing data plotting points and drawing line segments , surfaces, and other geometric forms or symbols.

What are the Ways to Represent Data?

Tables, charts, and graphs are all ways of representing data, and they can be used for two broad purposes. The first is to support the collection, organization, and analysis of data as part of the process of a scientific study.

What is the Objective of Graphical Representation of Data?

The main objective of representing data graphically is to display information visually that helps in understanding the information efficiently, clearly, and accurately. This is important to communicate the findings as well as analyze the data.

• Math Article

Graphical Representation

Graphical Representation is a way of analysing numerical data. It exhibits the relation between data, ideas, information and concepts in a diagram. It is easy to understand and it is one of the most important learning strategies. It always depends on the type of information in a particular domain. There are different types of graphical representation. Some of them are as follows:

• Line Graphs – Line graph or the linear graph is used to display the continuous data and it is useful for predicting future events over time.
• Bar Graphs – Bar Graph is used to display the category of data and it compares the data using solid bars to represent the quantities.
• Histograms – The graph that uses bars to represent the frequency of numerical data that are organised into intervals. Since all the intervals are equal and continuous, all the bars have the same width.
• Line Plot – It shows the frequency of data on a given number line. ‘ x ‘ is placed above a number line each time when that data occurs again.
• Frequency Table – The table shows the number of pieces of data that falls within the given interval.
• Circle Graph – Also known as the pie chart that shows the relationships of the parts of the whole. The circle is considered with 100% and the categories occupied is represented with that specific percentage like 15%, 56%, etc.
• Stem and Leaf Plot – In the stem and leaf plot, the data are organised from least value to the greatest value. The digits of the least place values from the leaves and the next place value digit forms the stems.
• Box and Whisker Plot – The plot diagram summarises the data by dividing into four parts. Box and whisker show the range (spread) and the middle ( median) of the data.

General Rules for Graphical Representation of Data

There are certain rules to effectively present the information in the graphical representation. They are:

• Suitable Title: Make sure that the appropriate title is given to the graph which indicates the subject of the presentation.
• Measurement Unit: Mention the measurement unit in the graph.
• Proper Scale: To represent the data in an accurate manner, choose a proper scale.
• Index: Index the appropriate colours, shades, lines, design in the graphs for better understanding.
• Data Sources: Include the source of information wherever it is necessary at the bottom of the graph.
• Keep it Simple: Construct a graph in an easy way that everyone can understand.
• Neat: Choose the correct size, fonts, colours etc in such a way that the graph should be a visual aid for the presentation of information.

Graphical Representation in Maths

In Mathematics, a graph is defined as a chart with statistical data, which are represented in the form of curves or lines drawn across the coordinate point plotted on its surface. It helps to study the relationship between two variables where it helps to measure the change in the variable amount with respect to another variable within a given interval of time. It helps to study the series distribution and frequency distribution for a given problem.  There are two types of graphs to visually depict the information. They are:

• Time Series Graphs – Example: Line Graph
• Frequency Distribution Graphs – Example: Frequency Polygon Graph

Principles of Graphical Representation

Algebraic principles are applied to all types of graphical representation of data. In graphs, it is represented using two lines called coordinate axes. The horizontal axis is denoted as the x-axis and the vertical axis is denoted as the y-axis. The point at which two lines intersect is called an origin ‘O’. Consider x-axis, the distance from the origin to the right side will take a positive value and the distance from the origin to the left side will take a negative value. Similarly, for the y-axis, the points above the origin will take a positive value, and the points below the origin will a negative value.

Generally, the frequency distribution is represented in four methods, namely

• Smoothed frequency graph
• Pie diagram
• Cumulative or ogive frequency graph
• Frequency Polygon

Merits of Using Graphs

Some of the merits of using graphs are as follows:

• The graph is easily understood by everyone without any prior knowledge.
• It saves time
• It allows us to relate and compare the data for different time periods
• It is used in statistics to determine the mean, median and mode for different data, as well as in the interpolation and the extrapolation of data.

Example for Frequency polygonGraph

Here are the steps to follow to find the frequency distribution of a frequency polygon and it is represented in a graphical way.

• Obtain the frequency distribution and find the midpoints of each class interval.
• Represent the midpoints along x-axis and frequencies along the y-axis.
• Plot the points corresponding to the frequency at each midpoint.
• Join these points, using lines in order.
• To complete the polygon, join the point at each end immediately to the lower or higher class marks on the x-axis.

Draw the frequency polygon for the following data

Mark the class interval along x-axis and frequencies along the y-axis.

Let assume that class interval 0-10 with frequency zero and 90-100 with frequency zero.

Now calculate the midpoint of the class interval.

Using the midpoint and the frequency value from the above table, plot the points A (5, 0), B (15, 4), C (25, 6), D (35, 8), E (45, 10), F (55, 12), G (65, 14), H (75, 7), I (85, 5) and J (95, 0).

To obtain the frequency polygon ABCDEFGHIJ, draw the line segments AB, BC, CD, DE, EF, FG, GH, HI, IJ, and connect all the points.

Frequently Asked Questions

What are the different types of graphical representation.

Some of the various types of graphical representation include:

• Line Graphs
• Frequency Table
• Circle Graph, etc.

Read More:  Types of Graphs

What are the Advantages of Graphical Method?

Some of the advantages of graphical representation are:

• It makes data more easily understandable.
• It saves time.
• It makes the comparison of data more efficient.

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Very useful for understand the basic concepts in simple and easy way. Its very useful to all students whether they are school students or college sudents

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Discover 20 Essential Types Of Graphs And Charts And When To Use Them

Table of Contents

1) What Are Graphs And Charts?

2) Charts And Graphs Categories

3) 20 Different Types Of Graphs And Charts

4) How To Choose The Right Chart Type

Data and statistics are all around us. It is very likely that you have found yourself looking at a chart or graph at work, in the news, sports, media, advertising, and many other places at some point in your life. That is because graphical representations of data make it easier to convey important information to different audiences. That said, there is still a lack of charting literacy due to the wide range of visuals available to us and the misuse of statistics . In many cases, even the chart designers are not picking the right visuals to convey the information in the correct way. 

So, how do you make sure you are using and understanding graphs and charts in the right way? In this post, we will provide you with the necessary knowledge to recognize the top 20 most commonly used types of charts and graphs and give insights into when and how to use them correctly. Each type of chart will have a visual example generated with datapine’s professional dashboard software . 

This knowledge will be valuable if you are a data visualization designer, a business user looking to incorporate visual analytics into his/her work, or just an average viewer looking to learn more about the topic. Let’s start this journey by looking at a definition. 

What Are Graphs And Charts?

A graph or chart is a graphical representation of qualitative or quantitative data. It uses different symbols such as bars, lines, columns, tables, box plots, maps, and more, to give meaning to the information, making it easier to understand than raw data. 

As you probably already know, multiple kinds of graphs and charts are widely used in various fields and industries such as business decision-making or research studies. These visual tools are used to find relationships between different data sets and extract valuable conclusions from them. In some cases, they are built by hand, and in others, most commonly, they are built using visualization tools. 

That said, the type of chart or graph you use will vary depending on the aim of the analysis. For instance, percentages are better viewed through a pie or bar chart while data that is changing over time is better viewed over a line chart. For that reason, it is important to have a clear understanding of the different chart types to make sure you are using the right one. 

Below we will discuss the graph and chart categories. These categories will build a solid foundation that will help you pick the right visual for your analytical aims. Let’s dive into them. 

Charts And Graphs Categories

As mentioned, asking the right questions will form the foundations for choosing the right types of visualization graphs for your project, strategy, or business goals. The fundamental categories that differentiate these questions are based on:

• Relationship : Understanding connections between different data points can significantly help discover new relevant insights. For instance, in the medical field, analyzing relationships between diseases and gene interactions can help discover a treatment for a particular disease. When it comes to visuals, a few graphics can help you easily identify and represent relationships. Scatter plots are valuable when you want to represent smaller data sets of two variables while bubble graphs are best for larger information with three or more variables. 
• Distribution: In statistics, distribution refers to the possibility of the occurrence of an outcome. To understand this, scientists and analysts use charts to represent the frequency distribution of the data and extract conclusions from it. For this purpose, they use line charts to analyze trends, scatter plots to highlight similarities across variables, and histograms to see the frequency distribution of a single variable across categories. 
• Composition : The purpose of business graphs and charts for composition is to compare parts to a whole in absolute numbers and normalized forms, usually a percentage. It is one of the most common and traditionally used visualization categories and it is usually limited by the simplicity of the chart types. Common composition graphs include pies, tree maps, and stacked bar charts. 
• Comparison: As its name suggests, this category refers to the comparison of multiple variables or multiple categories within a single variable. When comparing information it is fundamental to pick a chart that will make it easier to understand the differences. These differences can be within multiple elements, for example, top-selling products, or over time, such as the development of sales for different products over a year. For this purpose, tables, spiders, lines, columns, or area graphs are always a good choice.

To get a clearer impression, here is a visual overview of which chart to select based on what kind of data you need to show:

**click to enlarge**

Discover 20 Different Types Of Graphs And Charts

Now that you understand the key charting categories you are ready to dive into the main types of graphs and when to use them. Here, we will focus on the 20 most common types of visuals to represent your data in the most meaningful way possible. Each chart type has a visual example generated with datapine .

1) Number Chart

When to use 

A real-time number chart is essentially a ticker that will give you an immediate overview of a particular KPI . At a glance, you can see any total such as sales, percentage of evolution, number of visitors, etc. This is probably the easiest visualization type to build with the only consideration being the period you want to track. Do you want to show an entire history or simply the latest quarter? It is crucial to label the period clearly so your audience understands what story you are telling. Adding a trend indicator compares your number to the previous period (or to a fixed goal, depending on what you are tracking).

Other considerations

Number charts are often the first thing people see and are the quickest to read, so if there are too many, your narrative can get diluted. Using too many can also make your dashboard a little superficial. If you want more in-depth information, limit the number of number graphs and leave room for other types of data visualization that drill down a little deeper.

When you add a trend indicator, we suggest you compare numbers from the same period. For example, if you are tracking total sales for the current quarter, compare that data to the same quarter last year (or last period – depending on your story). If you select a target manually (perhaps you have no accurate past data), be sure to set realistic goals to be able to get on top of your KPI management practice. Again, remember to label the trend indicator clearly so your audience knows exactly what they are looking at.

2) Line Chart

The purpose of a line chart is to show trends, accelerations (or decelerations), and volatility. They display relationships in how data changes over a period of time. In our example above, we are showing Revenue by Payment Method for all of 2019 . Right away, you can see that the credit card payments were the highest and that everything took a dip in September. The takeaways are quick to register yet have depth.

Too many lines (variables) can make your chart complicated and hard to decipher. You may also find your audience constantly referencing the legend to remind them which one they are looking at. If you have too many variables, it’s time to consider a second (or even third) chart to tell this story.

When it comes to layout, keep your numbers relevant. When you set up your axis scale, keep it close to the highest data point. For example, if we had set the y-axis above to track all the way to 200K (when our highest data point is just over 90K), our chart would have been squished and hard to read. The top half would have been wasted space, and the data crammed. Let your data breathe a little!

One more thing!

A great feature of line graphs is that you can combine them with other types of visualizations, such as bar graphs. Using a double y-axis, one for the bar graph and one for the line, allows you to show two elements of your story in one graph. The primary y-axis below shows orders (bar graph), and the secondary y-axis is sales totals (line). The metrics are different and useful independently, but together, they tell a compelling story.

Maps are great at visualizing your geographic data by location. The information on a map is often displayed in a colored area map (like above) or a bubble map. Because maps are so effective at telling a story, they are used by governments, media, NGOs, nonprofits, public health departments – the list goes on. Maps aren’t just for displaying data; they also direct action. This was seen most recently through the Zika outbreak. Mapping the spread of the disease has helped health officials track it and effectively distribute resources where they are most needed.

Even if you aren’t saving the world from Zika, maps can help! For example, they are great at comparing your organization’s sales in different regions.

Everyone loves maps. However, that doesn’t mean you always need to display one. If the location isn’t a necessary part of your analytics story, you don’t need a map. They take up a lot of room, so only use them when necessary. Also, don’t just fill your maps with data points. Clickhole did a good job of satirizing this common data visualization type by placing 700 red dots on a map. Filling your map with data points doesn’t tell a compelling story; it just overwhelms the audience.

4) Waterfall Chart

This extremely useful chart depicts the power of visualizing data in a static, yet informative manner. It shows the composition of data over a set time period, illustrating the positive or negative values that help in understanding the overall cumulative effect. The decrements and increments can cause the cumulative to fall below or above the axis at various points, causing a clear overview of how the initial value is affected. It is often used in financial departments for analytical purposes, usually depicting the changes in revenue or profit. For example, your MRR ( monthly recurring revenue ), new revenue, upsell, lost, and current revenue. In our example above, we can conclude that our current revenue increased in our set time period.

Waterfall charts are static in their presentation so if you need to show dynamic data sets, then stacked graphs would be a better choice. Also, showing the relationship between selected multiple variables is not optimal for waterfall charts (also known as Cascade charts), as bubble plots or scatter plots would be a more effective solution.

5) Bar Graphs

There are three key types of bar graphs that we will cover in this section: Horizontal, Grouped and Stacked. Although all are in the same chart family, each serves a distinct purpose. Let’s discuss each of them in detail below. 

a) Horizontal Bar Graphs

Horizontal charts are perfect for comparative ranking, like a top-five list. They are also useful if your data labels are really long or complex. Keep them in an order that makes sense, though. Either list by value (like we did above) or, if that’s not the strength, choose a logic for the labels that makes sense, like listing them alphabetically.

Because time is best expressed left to right, it’s better to leave showing an evolution for the column chart. Also, like many charts, when you have too many values, a horizontal bar graph quickly becomes cluttered.

b) Grouped bar graph

When to Use 

Grouped bar charts follow the same logic as horizontal bars, except that they show values for two variables instead of one. The two variables are often displayed in disparate colors to help differentiate them from each other. It is recommended to use this chart type when you want to compare elements within a specific category or across other categories on the chart. For instance, in our example generated with a customer service analytics tool, we can see customer service tickets by channel divided between the total and solved ones. In this case, the grouped chart can help compare the values between the total and unsolved tickets as well as compare the number of solved tickets across channels and extract conclusions.  

Just like with the horizontal one, you need to be careful not to add too many categories into this graph type as they can make it look cluttered. The chart becomes difficult to read with the increase in categories, therefore, it is not the best when it comes to relationship or distribution analysis. 

c) Stacked bar chart

When to Use

A stacked bar chart is a variation of the traditional bar graph but, instead of dealing with one categorical variable, it deals with two or more. Each bar is divided into multiple subcategories that are compared with each other usually using a percentage. In the example above, the chart is comparing management and non-management positions (first categorical variable) that are being occupied by female and diverse employees vs male employees (second categorical variable). This allows the users of the bar to not only focus on the comparisons between the bars themselves but also extract conclusions based on the subcategories from each individual bar. 

When building a successful stacked bar graph it is important to carefully decide which of the two categorical variables will be the primary one. The primary one is the most important one and it will define the overall bar lengths. The secondary one will define the subcategories. Usually, if you are dealing with time ranges or numerical values, these make the best primary variables. However, it will vary from case to case. 

6) Column Graphs

When to use

Column charts are the standard for showing chronological data, such as growth over specific periods, and for comparing data across categories  (you can see this in the example where the accounts payable turnover is being compared based on date ranges). In general, for these kinds of charts, the categories are typically displayed on the horizontal axis while the numerical values are displayed vertically using rectangular columns. The size of the columns is proportional to the values displayed on the chart and their height allows people to easily extract conclusions at a glance. Unlike the bar chart which can display larger or more complex datasets, the column chart has a size limitation making it best to display smaller data. This makes it the go-to visualization for anyone looking for an easy and understandable way to display their information. 

Aside from the obvious design mistakes like using too many colors or too many categories, other things you want to make sure of are: if there is no natural order for the data (e.g. age categories or time ranges), it is recommended to order the values from higher to lowest or lowest to highest. Additionally, the y-axis should always start at 0, otherwise, the height of the columns can become misleading.  

c) Grouped column chart 

Just like the grouped bar chart, the grouped column chart compares two categorical variables instead of one using vertical columns instead of horizontal bars. The purpose of this graph is to see how the subcategories from the secondary variable change within each subcategory of the primary variable. Comparisons can be done within-group or between groups depending on the aim of the analysis.  In our sales data analysis example, Amount of Sales per Channel and Country (last year) , it is clear that we are comparing six regions and five channels. The color coding keeps the audience clued into which region we are referencing, and the proper spacing shows the channels (good design is at the heart of it all!). At a glance, you can see that SEM was the highest-earning channel, and with a little effort, the Netherlands stands out as the region that likely enjoyed the highest sales.

An important consideration when it comes to this graphic is to not use it to compare totals within the different levels of the categorical values. For this purpose, it is better to use a stacked column chart which we will discuss below.  

b) Stacked Column Chart

Stacked charts handle part-to-whole relationships. This is when you are comparing data to itself rather than seeing a total – often in the form of percentages. In the example above, the story isn’t about the total number of customers aged 15-25, but that 22% of the customers were 15-25 in the first quarter of 2014 (and 26% in Q4). The numbers we are working with are relative only to our total.

When showing single part-to-whole relationships, pie charts are the simplest way to go. Twenty-two percent of our customers are 15-25, leaving the other 78% to fit into the pie somehow. People get pie charts. They’re easy. But what if we want to show the same information over different periods? This would be a multiple part-to-whole relationship, and for this, we use a stacked bar graph. Again, we are telling the story of the percentage of customers in a certain age range, per quarter . The total number of each isn’t relevant here (although that information is used in the calculations). With proper spacing, we see each quarter clearly, and the color coding shows that overall, 46-55-year-olds are the most difficult customers to attract.

Aesthetically speaking, when you have too much data, columns become very thin and ugly. This also leaves little room to properly label your chart. Imagine we had 10 different age ranges per column. Some results, if not most, would be only slivers. To make your chart easy to understand, use good colors, proper spacing, and a balanced layout. This invites people to look at your chart and even enjoy it. A pretty chart is a much nicer way to consume data than squinting at a table.

7) Pie Charts

The much-maligned pie chart has had a bad couple of years. In fact, it has become pretty cliché to talk about how bad pie charts are. We understand the pie chart doesn’t do a lot, but it does do some things quite well. Pie charts are useful when demonstrating the proportional composition of a particular variable over a static timeframe. Let’s look at some particular cases:

• When the parts add up to 100%: The “part-to-whole relationship” is built right into it a pie chart in an obvious way. At a glance, any user knows a pie chart is splitting a population into parts and that the total of those parts equals 100%.
• When approximating is okay: The pie chart is particularly effective when eyeballing values are enough to get the conversation going. Also, it’s easier to estimate the percentage value of a pie chart compared to, let’s say, a bar chart. That’s because pies have an invisible scale with 25%, 50%, 75%, and 100% built-in at four points of the circle. Our eyes can easily decipher these proportions, driving the conversation about what variables do and don’t take up most of the pie. Your audience doesn’t have to guess the proportions – you can easily add data labels or build the sister of the pie chart, the donut chart, to display additional information.
• When there aren’t many proportions to the variable or they are combined: Pie charts are great when answering questions like, “What two largest suppliers control 65% of the market?”

Your audience isn’t always going to be comprised of data scientists. Accordingly, your presentation should be tailored to your particular audience. This brings us to another pie chart strength: people are familiar with pie charts. Any audience member will feel comfortable interpreting what the pie chart is presenting. As a bonus, circles generate more positive emotions: our brains like to look at circles over sharp corners. In the end, a pie chart simplifies the data story and encourages the audience.

Data visualization guru Edward Tufte famously declared that “pie charts are bad, and the only thing worse than one pie chart is lots of them.” We already talked about the pros of pie charts and why we don’t adhere to this strict no-pie-chart philosophy. We should also state that there are plenty of instances where you should not use a pie chart. First off, pie charts portray a stagnate time frame, so trending data is off the table with this visualization method. Make sure your audience understands the timeframe portrayed and try to document or label this applied filter somewhere.

Pie charts are also not the best types of data charts to make precise comparisons. This is especially true when there are multiple small pieces to the pie. If you need to see that one slice is 1% larger than another, it’s better to go with a bar chart. Another thing about multiple pieces to your pie – you don’t want too many. Pie charts are most effective when just displaying two portions. They lose presentation value after six segments. After six, it is hard for the eyes to decipher the slice's proportion. It also becomes difficult to label the pie chart, and valuable online dashboard /reporting real estate is often wasted in the process.

This brings us to the last issue: circles take up space. If you are using multiple pie charts in a dashboard, it is probably best to combine the data in one chart. We recommend checking out the stacked bar chart for these cases. You can also have a look at the different pie charts that are commonly used and explore the disadvantages of pie charts .

8) Gauge Charts

Gauge charts , also known as dial charts or speedometer charts, use needles and colors to show data similar to reading on a dial/speedometer, and they provide an easily digested visual. They are great for displaying a single value/measure within a quantitative context, such as to the previous period or to a target value. The gauge chart is often used in executive dashboards and reports to display progress against key business indicators. All you need to do is assign minimum and maximum values and define a color range, and the gauge chart will display an immediate trend indication.

Gauge charts are great for KPIs and single data points. After that, they can get a bit messy. With only one data point, you can’t easily compare different variables. You also can’t trend data using gauge charts. All of this makes taking actionable insight from a gauge chart difficult. Furthermore, they take up a lot of space – if your live dashboard has precious real estate, it may not be most efficient to fill it with multiple gauge charts. Using one chart to summarize multiple KPIs, you will likely get more bang for your buck.

9) Scatter Plot

Scatter plot is not only fun to say – it’s what you need when looking for the correlation in a large data set. The data sets need to be in pairs with a dependent variable and an independent variable. The dependent (the one the other relies on) becomes the y-axis, and the independent – the x-axis. When the data is distributed on the plot, the results show the correlation to be positive, negative (each to varying degrees), or nonexistent. Adding a trend line will help show the correlation and how statistically significant it is.

Scatter plots only work when you have a lot of data points and a correlation. If you are only talking about a few pieces of information, a scatter plot will be empty and pointless. The value comes through only when there are enough data points to see clear results. If you only have a little data or if your scatter plot shows no correlation at all, this chart has no place on your business dashboard. 

10) Spider Chart

Spider charts, or radar charts, are comparative charts used when multivariate data is displayed with three or more quantitative variables (aspects). This is useful when you want to evaluate two or more “things” using more than three aspects, all of which are similarly quantifiable. It’s certainly a mouthful, but it’s simple when you put it into use. Spider charts are great for rankings, appraisals, and reviews. For example, the three “things” we are comparing in our e-commerce example above are regions: Australia, Europe, and North America. The aspects we are comparing against are products sold are Cameras, TVs, Cell Phones, Games, and Computers. Each variable is being compared by how many units were sold – between 0 and 500. Europe is clearly outselling in all areas, and Australia is particularly weak in Cameras and Cell Phones. The concentration of strengths and weaknesses is evident at a glance.

This is not the easiest data analysis chart to pull off, but it really impresses when done correctly. Using this chart if you have more than five values in your dimension (five “things” to evaluate) makes it hard to read, which can make it pointless altogether. Whether you use solid lines or shaded areas, too many layers are difficult to interpret. Naturally, it is not a choice when you want to show time (the whole circular thing...).

We know – tables aren’t technically a graph. But sometimes, you really just need a table to portray your data in its raw format. With a table, you can display a large number of precise measures and dimensions. You can easily look up or compare individual values while also displaying grand totals. This is particularly beneficial when your audience needs to know the underlying data or get into the “weeds.” Tables are also effective if you have a diverse audience where each person wants to look at their own piece of the table. They are also great at portraying a lot of text or string values.

Remember – just because you are using a table doesn’t mean it can’t be visually pleasing. You can use various colors, border styles, font types, number formats, and icons to highlight and present your data effectively.

There are many reasons to use a table, but there are also many instances where different types of charts are a better choice. It all comes down to our eyes and brain. Tables interact primarily with the verbal system – we read tables. This reading includes processing the displayed information in a sequential fashion. Users read down columns or across rows of numbers, comparing one number to another. The keywords here are reading, processing, and time. Tables take longer to digest.

Graphs, on the other hand, are perceived by our visual system. They give numbers shape and form and tell a data story. They can present an immense amount of data quickly and in an easy-to-consume fashion. If data visualization is needed to identify patterns and relationships, a table is not the best choice. Also, while it is fun to get creative with colors, formatting, and icons, make sure your formatting and presentation choices are increasing your perception. The tables are hard enough to read as is!

12) Area Charts

The area chart is closely related to the line chart. Both chart types depict a time-series relationship, show continuity across a dataset, and are good for seeing trends rather than individual values. That said, there are some key differences between the two. Because of these differences, “when to use area charts” does not equal “when to use line charts.”

Line charts connect discrete but continuous data points through straight line segments. This makes them effective for facilitating trend analyses. Area charts technically do the same, except that the area below the plotted lines is filled with color. In this case, an un-stacked area chart is the same thing as a line chart – just with more coloring. The problem you run into here is occlusion: when you start comparing multiple variables/categories in an unstacked area chart, the upper layers obscure the lower layers. You can play around with transparency, but after three variables, un-stacked area charts are hard to read.

This brings us to the most commonly used area chart: the stacked area chart. Like stacked bar charts, stacked area charts portray a part-to-whole relationship. The total vertical of a stacked area chart shows the whole, while the height of each different dataset shows the parts. For example, a stacked area chart can show the sales trends for each region and the total sales trend. There are two different stacked area chart types you can use to portray the part-to-whole relationship.

Traditional Stacked Area Chart: The raw values are stacked, showing how the whole changes over time.

Stacked Percentage Area Chart: Percentages are stacked to show how the relationship between the different parts changes over time. This is best used to show the distribution of categories as parts of a whole where the cumulative total is less important.

As we hinted earlier, for the most part, you should stay away from un-stacked area charts. If you are just comparing 2-3 different variables that don’t obscure each other, then go ahead. But in general, they are often messy and don’t follow data visualization and dashboard design best practices . When it comes to stacked area charts, don’t use them when you don’t need to portray a part-to-whole relationship – use a line graph instead. Also, if you are trying to compare 7+ series, a stacked area graph becomes hard to read. In this case, you should once again turn to the line graph.

13) Bubble Plots

Bubble charts, or bubble graphs, are among the best types of data graphs for comparing several values or sets of data at a glance. If you’re looking to show the relationship between different product categories, revenue streams, investment risks, costs, or anything similar, bubble charts or plots are incredibly effective.

For instance, our example bubble plot showcases the relationship between a mix of retail product categories, primarily the number of orders and profit margin.

Here, you can tell that the TV & Home Theater product category has the highest number of orders (around 3,000 as you can see from the number scale on the left) as well as the highest profit margin, and therefore, it is the biggest bubble on the chart. Comparatively, the camera category shows the lowest number of orders in addition to the smallest profit margin and naturally is the smallest bubble on the chart.

The bubble plot is extremely powerful for visualizing two or more variables with multiple dimensions. And here, the bigger the bubble, the higher the profit margin. Not only are bubble plots visually stimulating, but they are also incredibly effective when building a comparative narrative for a specific audience.

It's difficult to go too far wrong with bubble charts, but the most common mistake with these types of business charts is focusing on varying the “radius” of the values rather than the “area” they take up on the chart. Doing so sometimes makes the bubbles on the plot disproportionate to the graph, making the information misleading at a glance. In short, your bubbles should be accurate in terms of size compared to the values. Get this right, and you’ll get the results you deserve. 

14) Boxplot 

Just like the histogram, the box plot is a graph that is used to represent the distribution of numeric data using boxes and lines. Each box is composed of five elements also known as the “Five-number summary” which are the minimum, first quartile, median (second quartile), third quartile, and maximum. Each of these elements represents a value and how it is distributed within the data set. Anything outside these values would be considered an outlier. An outlier is any value that is extremely high or extremely low compared to the nearest data point. Outliers (which are usually plotted as dots in the chart) need to be identified because they can affect the end result of the analysis and box plots are the best visuals to do so.  

Just like other types of charts on this list, box plots are not the best choice when it comes to big data sets. Their visual simplicity makes it hard to see details about the distribution results which makes it more difficult to interpret, especially when dealing with complex data. Plus, this chart works at its best when comparing different groups (as seen in our example above). So, if you are trying to look at the distribution of one single group a histogram is a better choice. 

15) Funnel chart 

As its name suggests, a funnel chart is a visualization type used to show how data moves or flows through a specific process. They are commonly utilized to display sales, recruitment, or order fulfillment funnels where the values are often decreasing as the funnel becomes smaller. This can be seen in the example above in which the number of potential clients decreases at each stage of the sales funnel. This is a natural progression that happens because not every person that shows interest in the opportunities stage will end up buying the product or subscribing to the service. 

In some cases, the sizes of the sections of the funnel chart are plotted proportionately with the value they are representing. This means the top section is 100% and the rest will represent their corresponding percentage with their size. This is not the case with our example in which the sections are sized to match the funnel shape, not the values contained in each section. 

Funnel graphics are very specific visuals that can only be used in particular cases. You should only use it if your data goes through a sequence of stages and the values are decreasing with each stage. Plus, they are only useful to represent a single variable which means they cannot be used to visualize relationships between variables. A good alternative for a funnel graph is a bar or column chart. 

16) Bullet chart 

A bullet chart is a variation of a bar or column chart but it provides some extra visual elements to give more context to the data. It is usually used for performance tracking to make comparisons against a goal or other relevant values and it is composed of three key elements. A single measure is represented by a darker shade bar with a length that represents the performance of that value, qualitative ranges are represented by lighter shades in the background, and a target or comparative measure which is represented with a small line that is perpendicular to the orientation of the graph. Bullet charts are great alternatives to gauge charts, especially when you are working with a KPI dashboard and don’t want to take up too much space from it. 

It is important to note that bullet charts are complex visuals that might be challenging to understand for non-technical audiences. In some cases, some people might choose to remove the shaded background to focus only on the actual value against the target or remove the target and focus on the qualitative ranges to make the chart friendlier to analyze.    This variation is also known as an overlapping bullet chart and it can be done using columns and bars, as we will see in our two examples below.

1. Overlapping bars bullet chart 

As we saw with different graph types previously, the bullet chart can be vertical (using columns) or horizontal (using bars). It is recommended to use bars when you want to display more categories or longer category names to avoid making the visual cluttered. In the example above, we can see the number of orders by product of the current year compared to a target. In this case, due to the number of products, a bar bullet graph is the best choice as it contains a lot of information without affecting the readability of the data.

2. Overlapping columns bullet chart 

On the other side, a bullet column chart is a better choice when you want to organize categories from left to right or when you have fewer categories to show. In this case, we can see the number of orders by product category. Given that product categories are fewer than the actual number of products, it is a good choice to pick columns to represent this data. In a traditional bullet chart, the number of orders by a quarter could be added for additional context as qualitative measures.

17) Treemap chart 

A treemap is a chart type used to display hierarchical data through rectangles that decrease their size as the value decreases, this process is also referred to as nesting. It is used to display large amounts of raw data in a visually appealing way that allows users to easily extract valuable conclusions at a glance. Its name comes from the shape of a tree, as the chart can be divided into multiple categories with different “branches” and “sub-branches”. Each of these categories should have a different color and the dimensions of the rectangles are based on the size of the data being displayed. 

Given that a treemap is used to visualize massive amounts of raw data, they can display an infinite amount of subcategories (or sub-branches) which can make them harder to understand. However, in most cases, users can drill down into the different categories to dig deeper into the data and answer different questions that might arise. 

If you are not trying to show hierarchical data then you should stay away from treemaps. Just like it happens with pie charts, this visualization is simply showing parts-to-whole relationships, therefore, it becomes useless for other purposes. You should also avoid treemaps if the data being displayed is too close in size. This defeats the purpose of the graph which is to easily identify the largest item from a specific category. A few alternatives for treemaps include column charts and scatter plots.

18) Stream charts 

A stream graph is considered a variation of the area chart with the difference that, while the area chart is plotted with a fixed x-axis, the stream graph has values displayed around a central axis. Hence, the flowing river-like shape. They are frequently used to identify trends and patterns in big datasets with multiple categories and evaluate how they change over time. Just like with other kinds of charts on this list, the width and length of the streams are proportional to the values being displayed. The colors can represent different categories or other specific criteria. In our example above, we can see the number of orders by product categories each month. The width of each stream can provide valuable insights into the performance of each category. For instance, from June to August orders for TVs and Home Theaters decreased a lot compared to other months so some conclusions need to be drawn.

In general, if your aim is to use the chart to deeply analyze the data and extract conclusions from it, then the stream is not your best option. They are often cluttered with a lot of information which can lead to legibility issues. This can happen especially when you have smaller categories that end up looking way too small compared to bigger ones. For that reason, it is best to use stream charts as interactive visuals instead of static or printed ones. 

19) Word Cloud 

A word cloud is a straightforward type of graph that displays a set of words concerning a specific topic. The words are arranged in different directions and the sizes of the words will vary depending on specific criteria. For example, if a word cloud is generated based on a text from product reviews, the size of the words can be influenced by the number of times each word is mentioned within the text. On the other hand, if you are generating a word cloud of all the countries in the world, the names of the countries can be bigger or smaller depending on their population. From an analytical perspective, word clouds don’t provide a lot of value apart from being an engaging and visually appealing way of presenting a topic or supporting discussions. 

There is no general rule when it comes to colors on a word cloud. Some might use different colors to provide meaning to certain words while others might use standard colors to match their branding. Whichever case you are using, the rule of not adding too many colors to avoid overcrowding the visual still applies when it comes to word clouds. 

20) Progress chart 

As its name suggests, this chart is used to track the progress of a specific activity or scenario usually in a percentage form. It can be represented using bars or columns and is often tracked against a set target, as seen in the graph examples below, in which you can see a colored area representing the completed percentage and a lighter shade representing the remaining percentage to complete 100%. Progress graphs are wildly popular when tracking the development of a project as they provide a clear overview of the status of different tasks. They are also valuable visuals when you are trying to show any kind of percentage value or progress against a target. 

Progress charts are very straightforward and don’t provide a lot more information than the development of a metric. If you want to gain more insights you can explore using a bullet chart as they provide more context to the data. 

• Progress bar graph 

The progress bar chart is used to track the progress of a specific activity or metric using horizontal bars. The example above is tracking the percentage of purchases in time and budget from a procurement department. Ideally, the end goal for each category would be 100% as this means all purchases are made on the expected time and budget. However, this is not always the case and the progress bar is a great way to see how far from the expected target the values actually are. In this case, the average is represented by a darker color of green, and the remaining percentage to reach 100% is represented by a lighter shade.  

• Progress column graph 

The progress column chart is a type of progress chart that uses columns to represent different data values. In this case, our example is showing the average time to fill a position by the department where each department has a predefined target they are expected to reach. In this case, the target value is represented by a dark purple dot, in other cases, it could be represented with a lighter shade of the same purple from the column. Using a progress chart to represent this metric is a great way to compare the different departments and see if any of the processes need to be optimized to better reach the expected target. 

How To Choose The Right Chart Type: 9 Essential Questions To Ask

To go further into detail, we have selected the top 9 questions you need to consider to ensure success from the very start of your journey.

1. What story do you want to tell?

At its core, data charts are about taking data and transforming it into actionable insight by using visuals to tell a story. Data-driven storytelling is a powerful force as it takes stats and metrics and puts them into context through a narrative that everyone inside or outside of the organization can understand.

By asking yourself what kind of story you want to tell with your data and what message you want to convey to your audience, you’ll be able to choose the right data visualization types for your project or initiative. And ultimately, you’re likely to enjoy the results you're aiming for.

For more on data storytelling, check out our full guide for dashboard presentation and storytelling.

2. Who do you want to tell it to?

Another key element of choosing the right data visualization types is gaining a clear understanding of who you want to tell your story to – or in other words, asking yourself the question, “ Who is my audience ?”

You may be aiming your data visualization efforts at a particular team within your organization, or you may be trying to communicate a set of trends or predictive insights to a selection of corporate investors. Take the time to research your audience, and you’ll be able to make a more informed decision on which data visualization chart types will make the most tangible connection with the people you’ll be presenting your findings to.

3. How big is your data? 

As you probably learned from our list of the essential types of charts and when to use them, the size of your data will significantly affect the type of visualization you decide to use. Some charts are not meant to be used with massive amounts of data due to design aspects while others are perfect for displaying larger information. 

For example, pie charts are not good if you are trying to show multiple categories. For that purpose, a scatter plot works best. Another example is with column and bar charts. Bar charts use horizontal bars that make it easier to represent larger data sets. On the other side, column charts are limited by size due to their vertical orientation, making them better for smaller data. 

4. What is the type of data you are using? 

Another important question to ask yourself is what type of data you are using. As we saw at the beginning of the post, there are 4 key categories when it comes to data visualization: composition, distribution, relationship, and comparison. There are also qualitative and quantitative data that can be better represented using a particular graphic. For this reason, it is important to carefully define the type of data you are using before thinking about visualizing it. In the following questions, we will see what you need to ask yourself based on the mentioned categories. 

5. Are you looking to analyze particular trends?

Every data visualization project or initiative is slightly different, which means that different data visualization chart types will suit varying goals, aims, or topics.

After gaining a greater level of insight into your audience as well as the type of story you want to tell, you should decide whether you're looking to communicate a particular trend relating to a particular data set, over a predetermined time period. What will work best?

• Line charts
• Column charts
• Area charts

6. Do you want to demonstrate the composition of your data?

If your primary aim is to showcase the composition of your data – in other words, show how individual segments of data make up the whole of something – choosing the right types of data visualizations is crucial in preventing your message from becoming lost or diluted.

In these cases, the most effective types of visual charts include:

• Waterfall charts
• Stacked charts
• Map-based graphs (if your information is geographical)

7. Do you want to compare two or more sets of values?

While most types of data visualizations will allow you to compare two or more trends or data sets, there are certain graphs or charts that will make your message all the more powerful.

If your main goal is to show a direct comparison between two or more sets of information, the best choice would be:

• Bubble charts
• Spider charts
• Columned visualizations
• Scatter plots

Data visualization is based on painting a picture with your data rather than leaving it sitting static in a spreadsheet or table. Technically, any way you choose to do this count, but as outlined here, there are some charts that are way better at telling a specific story.

8. Is timeline a factor?

By understanding whether the data you’re looking to extract value from is time-based or time-sensitive, you’ll be able to select a graph or chart that will provide you with an instant overview of figures or comparative trends over a specific period.

In these instances, incredibly effective due to their logical, data-centric designs, functionality and features are:

• Dynamic line charts

9. How do you want to show your KPIs?

It’s important to ask yourself how you want to showcase your key performance indicators as not only will this dictate the success of your analytical activities but it will also determine how clearly your visualizations or data-driven stories resonate with your audience.

Consider what information you’re looking to gain from specific KPIs within your campaigns or activities and how they will resonate with those that you’ll be sharing the information with - if necessary, experiment with different formats until you find the graphs or charts that fit your goals exactly.

Here are two simple bonus questions to help make your data visualization types even more successful:

• Are you comparing data or demonstrating a relationship?
• Would you like to demonstrate a trend?

At datapine, data visualization is our forte. We know what it takes to make a good dashboard – and this means crafting a visually compelling and coherent story.

"Visualization gives you answers to questions you didn’t know you had." – Ben Shneiderman

Design-thinking In Data Visualization

When it comes to different data visualization types, there is no substitute for a solid design. If you take the time to understand the reason for your data visualization efforts, the people you’re aiming them at, and the approaches you want to take to tell your story, you will yield great results.

Here at datapine, we’ve developed the very best design options for our dashboard reporting software , making them easy to navigate yet sophisticated enough to handle all your data in a way that matters.

With our advanced dashboard features , including a host of global styling options, we enable you to make your dashboard as appealing as possible to the people being presented with your data.

Your part in creating an effective design for the different types of data charts boils down to choosing the right visualization to tell a coherent, inspiring, and widely accessible story. Rarely will your audience understand how much strategic thought you have put into your selection of dashboards – as with many presentational elements, the design is often undervalued. However, we understand how important this is, and we’re here to lend a helping hand.

In this guide, we covered different types of charts to represent data, explored key questions you need to ask yourself to choose the right ones, and saw examples of graphs to put their value into perspective. By now, you should have a better understanding of how each type of visual works and how you can use them to convey your message correctly. 

To summarize, here are the top types of charts and their uses:

• Number Chart - gives an immediate overview of a specific value .
• Line Chart - shows trends and changes in data over a period of time .
• Maps - visualizes data by geographical location.
• Waterfall Chart - demonstrates the static composition of data.
• Bar Graphs - used to compare data of large or more complex items .
• Column Chart - used to compare data of smaller items. 
• Gauge Chart - used to display a single value within a quantitative context.
• Pie Chart - indicates the proportional composition of a variable.
• Scatter Plot - applied to express relations and distribution of large sets of data.
• Spider Chart - comparative charts great for rankings, reviews, and appraisals.
• Tables - show a large number of precise dimensions and measures .
• Area Chart - portrays a part-to-whole relationship over time .
• Bubble Plots - visualizes 2 or more variables with multiple dimensions.
• Boxplot -  shows data distribution within multiple groups.
• Funnel Chart - to display how data moves through a process.
• Bullet Chart - comparing the performance of one or more primary measures .
• Treemap - to plot large volumes of hierarchical data across various categories.
• Stream Graph - shows trends and patterns over time in large volumes of data.
• Word Cloud - to observe the frequency of words within a text.  
• Progress Chart - displays progress against a set target or goal.

But in our hyper-connected digital age, there are many more different kinds of graphs you can use to your advantage. Putting everything together in a professional business dashboard is even better. These visual tools provide centralized access to your most important data to get a 360-view of your performance so you can optimize it and ensure continuous growth.

Complete with stunning visuals, our advanced online data visualization software can make it easy for you to manipulate your data and visualize it using professional dashboards. The best part is, you can try it for a 14-day trial , completely free!

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Home » Graphical Methods – Types, Examples and Guide

Graphical Methods – Types, Examples and Guide

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Graphical Methods

Definition:

Graphical methods refer to techniques used to visually represent data, relationships, or processes using charts, graphs, diagrams, or other graphical formats. These methods are widely used in various fields such as science, engineering, business, and social sciences, among others, to analyze, interpret and communicate complex information in a concise and understandable way.

Types of Graphical Methods

Here are some of the most common types of graphical methods for data analysis and visual presentation:

Line Graphs

These are commonly used to show trends over time, such as the stock prices of a particular company or the temperature over a certain period. They consist of a series of data points connected by a line that shows the trend of the data over time. Line graphs are useful for identifying patterns in data, such as seasonal changes or long-term trends.

These are commonly used to compare values of different categories, such as sales figures for different products or the number of students in different grade levels. Bar charts use bars that are either horizontal or vertical and represent the data values. They are useful for comparing data visually and identifying differences between categories.

These are used to show how a whole is divided into parts, such as the percentage of students in a school who are enrolled in different programs. Pie charts use a circle that is divided into sectors, with each sector representing a portion of the whole. They are useful for showing proportions and identifying which parts of a whole are larger or smaller.

Scatter Plots

These are used to visualize the relationship between two variables, such as the correlation between a person’s height and weight. Scatter plots consist of a series of data points that are plotted on a graph and connected by a line or curve. They are useful for identifying trends and relationships between variables.

These are used to show the distribution of data across a two-dimensional plane, such as a map of a city showing the density of population in different areas. Heat maps use color-coded cells to represent different levels of data, with darker colors indicating higher values. They are useful for identifying areas of high or low density and for highlighting patterns in data.

These are used to show the distribution of data in a single variable, such as the distribution of ages of a group of people. Histograms use bars that represent the frequency of each data value, with taller bars indicating a higher frequency. They are useful for identifying the shape of a distribution and for identifying outliers or unusual data values.

Network Diagrams

These are used to show the relationships between different entities or nodes, such as the relationships between people in a social network. Network diagrams consist of nodes that are connected by lines that represent the relationship. They are useful for identifying patterns in complex data and for understanding the structure of a network.

Box plots, also known as box-and-whisker plots, are a type of graphical method used to show the distribution of data in a single variable. They consist of a box with whiskers extending from the top and bottom of the box. The box represents the middle 50% of the data, with the median value indicated by a line inside the box. The whiskers represent the range of the data, with any data points outside the whiskers indicated as outliers. Box plots are useful for identifying the spread and shape of a distribution and for identifying outliers or unusual data values.

Applications of Graphical Methods

Graphical methods have a wide range of applications in various fields, including:

• Business : Graphical methods are commonly used in business to analyze sales data, financial data, and other types of data. They are useful for identifying trends, patterns, and outliers, as well as for presenting data in a clear and concise manner to stakeholders.
• Science and engineering: Graphical methods are used extensively in scientific and engineering fields to analyze data and to present research findings. They are useful for visualizing complex data sets and for identifying relationships between variables.
• Social sciences: Graphical methods are used in social sciences to analyze and present data related to human behavior, such as demographics, survey results, and statistical analyses. They are useful for identifying trends and patterns in large data sets and for communicating findings to a broader audience.
• Education : Graphical methods are used in education to present information to students and to help them understand complex concepts. They are useful for visualizing data and for presenting information in a way that is easy to understand.
• Healthcare : Graphical methods are used in healthcare to analyze patient data, to track disease outbreaks, and to present medical information to patients. They are useful for identifying patterns and trends in patient data and for communicating medical information in a clear and concise manner.
• Sports : Graphical methods are used in sports to analyze and present data related to player performance, team statistics, and game outcomes. They are useful for identifying trends and patterns in player and team data and for communicating this information to coaches, players, and fans.

Examples of Graphical Methods

Here are some examples of real-time applications of graphical methods:

• Stock Market: Line graphs, candlestick charts, and bar charts are widely used in real-time trading systems to display stock prices and trends over time. Traders use these charts to analyze historical data and make informed decisions about buying and selling stocks in real-time.
• Weather Forecasting : Heat maps and radar maps are commonly used in weather forecasting to display current weather conditions and to predict future weather patterns. These maps are useful for tracking the movement of storms, identifying areas of high and low pressure, and predicting the likelihood of severe weather events.
• Social Media Analytics: Scatter plots and network diagrams are commonly used in social media analytics to track the spread of information across social networks. Analysts use these graphs to identify patterns in user behavior, to track the popularity of specific topics or hashtags, and to monitor the influence of key opinion leaders.
• Traffic Analysis: Heat maps and network diagrams are used in traffic analysis to visualize traffic flow patterns and to identify areas of congestion or accidents. These graphs are useful for predicting traffic patterns, optimizing traffic flow, and improving transportation infrastructure.
• Medical Diagnostics: Box plots and histograms are commonly used in medical diagnostics to display the distribution of patient data, such as blood pressure, heart rate, or blood sugar levels. These graphs are useful for identifying patterns in patient data, diagnosing medical conditions, and monitoring the effectiveness of treatments in real-time.
• Cybersecurity: Heat maps and network diagrams are used in cybersecurity to visualize network traffic patterns and to identify potential security threats. These graphs are useful for identifying anomalies in network traffic, detecting and mitigating cyber attacks, and improving network security protocols.

How to use Graphical Methods

Here are some general steps to follow when using graphical methods to analyze and present data:

• Identify the research question: Before creating any graphs, it’s important to identify the research question or hypothesis you want to explore. This will help you select the appropriate type of graph and ensure that the data you collect is relevant to your research question.
• Collect and organize the data: Collect the data you need to answer your research question and organize it in a way that makes it easy to work with. This may involve sorting, filtering, or cleaning the data to ensure that it is accurate and relevant.
• Select the appropriate graph : There are many different types of graphs available, each with its own strengths and weaknesses. Select the appropriate graph based on the type of data you have and the research question you are exploring. For example, a scatterplot may be appropriate for exploring the relationship between two continuous variables, while a bar chart may be appropriate for comparing categorical data.
• Create the graph: Once you have selected the appropriate graph, create it using software or a tool that allows you to customize the graph based on your needs. Be sure to include appropriate labels and titles, and ensure that the graph is clearly legible.
• Analyze the graph: Once you have created the graph, analyze it to identify patterns, trends, and relationships in the data. Look for outliers or other anomalies that may require further investigation.
• Draw conclusions: Based on your analysis of the graph, draw conclusions about the research question you are exploring. Use the graph to support your conclusions and to communicate your findings to others.
• Iterate and refine: Finally, refine your graph or create additional graphs as needed to further explore your research question. Iteratively refining and revising your graphs can help to ensure that you are accurately representing the data and that you are drawing the appropriate conclusions.

When to use Graphical Methods

Graphical methods can be used in a variety of situations to help analyze, interpret, and communicate data. Here are some general guidelines on when to use graphical methods:

• To identify patterns and trends: Graphical methods are useful for identifying patterns and trends in data, which may be difficult to see in raw data tables or spreadsheets. Graphs can reveal trends that may not be immediately apparent in the data, making it easier to draw conclusions and make predictions.
• To compare data: Graphs can be used to compare data from different sources or over different time periods. Graphical comparisons can make it easier to identify differences or similarities in the data, which can be useful for making decisions and taking action.
• To summarize data : Graphs can be used to summarize large amounts of data in a single visual display. This can be particularly useful when presenting data to a broad audience, as it can help to simplify complex data sets and make them more accessible.
• To communicate data: Graphs can be used to communicate data and findings to a variety of audiences, including stakeholders, colleagues, and the general public. Graphs can be particularly useful in situations where data needs to be presented quickly and in a way that is easy to understand.
• To identify outliers: Graphical methods are useful for identifying outliers or anomalies in the data. Outliers can be indicative of errors or unusual events, and may warrant further investigation.

Purpose of Graphical Methods

The purpose of graphical methods is to help people analyze, interpret, and communicate data in a way that is both accurate and understandable. Graphical methods provide visual representations of data that can be easier to interpret than tables of numbers or raw data sets. Graphical methods help to reveal patterns and trends that may not be immediately apparent in the data, making it easier to draw conclusions and make predictions. They can also help to identify outliers or unusual data points that may warrant further investigation.

In addition to helping people analyze and interpret data, graphical methods also serve an important communication function. Graphs can be used to present data to a wide range of audiences, including stakeholders, colleagues, and the general public. Graphs can help to simplify complex data sets, making them more accessible and easier to understand. By presenting data in a clear and concise way, graphical methods can help people make informed decisions and take action based on the data.

Overall, the purpose of graphical methods is to provide a powerful tool for analyzing, interpreting, and communicating data. Graphical methods help people to better understand the data they are working with, to identify patterns and trends, and to make informed decisions based on the data.

Characteristics of Graphical Methods

Here are some characteristics of graphical methods:

• Visual Representation: Graphical methods provide a visual representation of data, which can be easier to interpret than tables of numbers or raw data sets. Graphs can help to reveal patterns and trends that may not be immediately apparent in the data.
• Simplicity : Graphical methods simplify complex data sets, making them more accessible and easier to understand. By presenting data in a clear and concise way, graphical methods can help people make informed decisions and take action based on the data.
• Comparability : Graphical methods can be used to compare data from different sources or over different time periods. This can help to identify differences or similarities in the data, which can be useful for making decisions and taking action.
• Flexibility : Graphical methods can be adapted to different types of data, including continuous, categorical, and ordinal data. Different types of graphs can be used to display different types of data, depending on the characteristics of the data and the research question.
• Accuracy : Graphical methods should accurately represent the data being analyzed. Graphs should be properly scaled and labeled to avoid distorting the data or misleading viewers.
• Clarity : Graphical methods should be clear and easy to read. Graphs should be designed with the viewer in mind, using appropriate colors, labels, and titles to ensure that the message of the graph is conveyed effectively.

Advantages of Graphical Methods

Graphical methods offer several advantages for analyzing and presenting data, including:

• Clear visualization: Graphical methods provide a clear and intuitive visual representation of data that can help people understand complex relationships, trends, and patterns in the data. This can be particularly useful when dealing with large and complex data sets.
• Efficient communication: Graphical methods can help to communicate complex data sets in an efficient and accessible way. Visual representations can be easier to understand than numerical data alone, and can help to convey key messages quickly.
• Effective comparison: Graphical methods allow for easy comparison between different data sets, making it easier to identify trends, patterns, and differences. This can help in making decisions, identifying areas for improvement, or developing new insights.
• Improved decision-making: Graphical methods can help to inform decision-making by presenting data in a clear and easy-to-understand format. They can also help to identify key areas of focus, enabling individuals or teams to make more informed decisions.
• Increased engagement: Graphical methods can help to engage audiences by presenting data in an engaging and interactive way. This can be particularly useful in presentations or reports, where visual representations can help to maintain audience attention and interest.
• Better understanding: Graphical methods can help individuals to better understand the data they are working with, by providing a clear and intuitive visual representation of the data. This can lead to improved insights and decision-making, as well as better understanding of the implications of the data.

Limitations of Graphical Methods

Here are a few limitations to consider:

• Misleading representation: Graphical methods can potentially misrepresent data if they are not designed properly. For example, inappropriate scaling or labeling of the axes or the use of certain types of graphs can create a distorted view of the data.
• Limited scope: Graphical methods can only display a limited amount of data, which can make it difficult to capture the full complexity of a data set. Additionally, some types of data may be difficult to represent visually.
• Time-consuming : Creating graphs can be a time-consuming process, particularly if multiple graphs need to be created and analyzed. This can be a limitation in situations where time is limited or resources are scarce.
• Technical skills: Some graphical methods require technical skills to create and interpret. For example, certain types of graphs may require knowledge of specialized software or programming languages.
• Interpretation : Interpreting graphs can be subjective, and the same graph can be interpreted in different ways by different people. This can lead to confusion or disagreements when using graphs to communicate data.
• Accessibility : Some graphical methods may not be accessible to all audiences, particularly those with visual impairments. Additionally, some types of graphs may not be accessible to those with limited literacy or numeracy skills.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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2.E: Graphical Representations of Data (Exercises)

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2.2: Stem-and-Leaf Graphs (Stemplots), Line Graphs, and Bar Graphs

Student grades on a chemistry exam were: 77, 78, 76, 81, 86, 51, 79, 82, 84, 99

• Construct a stem-and-leaf plot of the data.
• Are there any potential outliers? If so, which scores are they? Why do you consider them outliers?

The table below contains the 2010 obesity rates in U.S. states and Washington, DC.

• Use a random number generator to randomly pick eight states. Construct a bar graph of the obesity rates of those eight states.
• Construct a bar graph for all the states beginning with the letter "A."
• Construct a bar graph for all the states beginning with the letter "M."
• Number the entries in the table 1–51 (Includes Washington, DC; Numbered vertically)
• Arrow over to PRB
• Press 5:randInt(
• Enter 51,1,8)

Eight numbers are generated (use the right arrow key to scroll through the numbers). The numbers correspond to the numbered states (for this example: {47 21 9 23 51 13 25 4}. If any numbers are repeated, generate a different number by using 5:randInt(51,1)). Here, the states (and Washington DC) are {Arkansas, Washington DC, Idaho, Maryland, Michigan, Mississippi, Virginia, Wyoming}.

Corresponding percents are {30.1, 22.2, 26.5, 27.1, 30.9, 34.0, 26.0, 25.1}.

Figure \(\PageIndex{1}\): (a)

Figure \(\PageIndex{1}\): (b)

Figure \(\PageIndex{1}\): (c)

For each of the following data sets, create a stem plot and identify any outliers.

Exercise 2.2.7

The miles per gallon rating for 30 cars are shown below (lowest to highest).

19, 19, 19, 20, 21, 21, 25, 25, 25, 26, 26, 28, 29, 31, 31, 32, 32, 33, 34, 35, 36, 37, 37, 38, 38, 38, 38, 41, 43, 43

The height in feet of 25 trees is shown below (lowest to highest).

25, 27, 33, 34, 34, 34, 35, 37, 37, 38, 39, 39, 39, 40, 41, 45, 46, 47, 49, 50, 50, 53, 53, 54, 54

The data are the prices of different laptops at an electronics store. Round each value to the nearest ten.

249, 249, 260, 265, 265, 280, 299, 299, 309, 319, 325, 326, 350, 350, 350, 365, 369, 389, 409, 459, 489, 559, 569, 570, 610

The data are daily high temperatures in a town for one month.

61, 61, 62, 64, 66, 67, 67, 67, 68, 69, 70, 70, 70, 71, 71, 72, 74, 74, 74, 75, 75, 75, 76, 76, 77, 78, 78, 79, 79, 95

For the next three exercises, use the data to construct a line graph.

Exercise 2.2.8

In a survey, 40 people were asked how many times they visited a store before making a major purchase. The results are shown in the Table below.

Exercise 2.2.9

In a survey, several people were asked how many years it has been since they purchased a mattress. The results are shown in Table .

Exercise 2.2.10

Several children were asked how many TV shows they watch each day. The results of the survey are shown in the Table below.

Exercise 2.2.11

The students in Ms. Ramirez’s math class have birthdays in each of the four seasons. Table shows the four seasons, the number of students who have birthdays in each season, and the percentage (%) of students in each group. Construct a bar graph showing the number of students.

Using the data from Mrs. Ramirez’s math class supplied in the table above, construct a bar graph showing the percentages.

Exercise 2.2.12

David County has six high schools. Each school sent students to participate in a county-wide science competition. Table shows the percentage breakdown of competitors from each school, and the percentage of the entire student population of the county that goes to each school. Construct a bar graph that shows the population percentage of competitors from each school.

Use the data from the David County science competition supplied in Exercise . Construct a bar graph that shows the county-wide population percentage of students at each school.

2.3: Histograms, Frequency, Polygons, and Time Series Graphs

Suppose that three book publishers were interested in the number of fiction paperbacks adult consumers purchase per month. Each publisher conducted a survey. In the survey, adult consumers were asked the number of fiction paperbacks they had purchased the previous month. The results are as follows:

• Find the relative frequencies for each survey. Write them in the charts.
• Using either a graphing calculator, computer, or by hand, use the frequency column to construct a histogram for each publisher's survey. For Publishers A and B, make bar widths of one. For Publisher C, make bar widths of two.
• In complete sentences, give two reasons why the graphs for Publishers A and B are not identical.
• Would you have expected the graph for Publisher C to look like the other two graphs? Why or why not?
• Make new histograms for Publisher A and Publisher B. This time, make bar widths of two.
• Now, compare the graph for Publisher C to the new graphs for Publishers A and B. Are the graphs more similar or more different? Explain your answer.

Often, cruise ships conduct all on-board transactions, with the exception of gambling, on a cashless basis. At the end of the cruise, guests pay one bill that covers all onboard transactions. Suppose that 60 single travelers and 70 couples were surveyed as to their on-board bills for a seven-day cruise from Los Angeles to the Mexican Riviera. Following is a summary of the bills for each group.

• Fill in the relative frequency for each group.
• Construct a histogram for the singles group. Scale the x -axis by $50 widths. Use relative frequency on the y -axis.
• Construct a histogram for the couples group. Scale the x -axis by $50 widths. Use relative frequency on the y -axis.
• List two similarities between the graphs.
• List two differences between the graphs.
• Overall, are the graphs more similar or different?
• Construct a new graph for the couples by hand. Since each couple is paying for two individuals, instead of scaling the x -axis by $50, scale it by $100. Use relative frequency on the y -axis.
• How did scaling the couples graph differently change the way you compared it to the singles graph?
• Based on the graphs, do you think that individuals spend the same amount, more or less, as singles as they do person by person as a couple? Explain why in one or two complete sentences.
• See the tables above

• Both graphs have a single peak.
• Both graphs use class intervals with width equal to $50.
• The couples graph has a class interval with no values.
• It takes almost twice as many class intervals to display the data for couples.
• Answers may vary. Possible answers include: The graphs are more similar than different because the overall patterns for the graphs are the same.
• Check student's solution.
• Both graphs display 6 class intervals.
• Both graphs show the same general pattern.
• Answers may vary. Possible answers include: Although the width of the class intervals for couples is double that of the class intervals for singles, the graphs are more similar than they are different.
• Answers may vary. Possible answers include: You are able to compare the graphs interval by interval. It is easier to compare the overall patterns with the new scale on the Couples graph. Because a couple represents two individuals, the new scale leads to a more accurate comparison.
• Answers may vary. Possible answers include: Based on the histograms, it seems that spending does not vary much from singles to individuals who are part of a couple. The overall patterns are the same. The range of spending for couples is approximately double the range for individuals.

Twenty-five randomly selected students were asked the number of movies they watched the previous week. The results are as follows.

• Construct a histogram of the data.
• Complete the columns of the chart.

Use the following information to answer the next two exercises: Suppose one hundred eleven people who shopped in a special t-shirt store were asked the number of t-shirts they own costing more than $19 each.

The percentage of people who own at most three t-shirts costing more than $19 each is approximately:

• Cannot be determined

If the data were collected by asking the first 111 people who entered the store, then the type of sampling is:

• simple random
• convenience

Following are the 2010 obesity rates by U.S. states and Washington, DC.

Construct a bar graph of obesity rates of your state and the four states closest to your state. Hint: Label the \(x\)-axis with the states.

Answers will vary.

Exercise 2.3.6

Sixty-five randomly selected car salespersons were asked the number of cars they generally sell in one week. Fourteen people answered that they generally sell three cars; nineteen generally sell four cars; twelve generally sell five cars; nine generally sell six cars; eleven generally sell seven cars. Complete the table.

Exercise 2.3.7

What does the frequency column in the Table above sum to? Why?

Exercise 2.3.8

What does the relative frequency column in in the Table above  sum to? Why?

Exercise 2.3.9

What is the difference between relative frequency and frequency for each data value in in the Table above ?

The relative frequency shows the proportion of data points that have each value. The frequency tells the number of data points that have each value.

Exercise 2.3.10

What is the difference between cumulative relative frequency and relative frequency for each data value?

Exercise 2.3.11

To construct the histogram for the data in in the Table above , determine appropriate minimum and maximum x and y values and the scaling. Sketch the histogram. Label the horizontal and vertical axes with words. Include numerical scaling.

Answers will vary. One possible histogram is shown:

Exercise 2.3.12

Construct a frequency polygon for the following:

Exercise 2.3.13

Construct a frequency polygon from the frequency distribution for the 50 highest ranked countries for depth of hunger.

Find the midpoint for each class. These will be graphed on the x -axis. The frequency values will be graphed on the y -axis values.

Exercise 2.3.14

Use the two frequency tables to compare the life expectancy of men and women from 20 randomly selected countries. Include an overlayed frequency polygon and discuss the shapes of the distributions, the center, the spread, and any outliers. What can we conclude about the life expectancy of women compared to men?

Exercise 2.3.15

Construct a times series graph for (a) the number of male births, (b) the number of female births, and (c) the total number of births.

Exercise 2.3.16

The following data sets list full time police per 100,000 citizens along with homicides per 100,000 citizens for the city of Detroit, Michigan during the period from 1961 to 1973.

• Construct a double time series graph using a common x -axis for both sets of data.
• Which variable increased the fastest? Explain.
• Did Detroit’s increase in police officers have an impact on the murder rate? Explain.

2.4: Measures of the Location of the Data

The median age for U.S. blacks currently is 30.9 years; for U.S. whites it is 42.3 years.

• Based upon this information, give two reasons why the black median age could be lower than the white median age.
• Does the lower median age for blacks necessarily mean that blacks die younger than whites? Why or why not?
• How might it be possible for blacks and whites to die at approximately the same age, but for the median age for whites to be higher?

Six hundred adult Americans were asked by telephone poll, "What do you think constitutes a middle-class income?" The results are in the Table below. Also, include left endpoint, but not the right endpoint.

• What percentage of the survey answered "not sure"?
• What percentage think that middle-class is from $25,000 to $50,000?
• Should all bars have the same width, based on the data? Why or why not?
• How should the <20,000 and the 100,000+ intervals be handled? Why?
• Find the 40 th and 80 th percentiles
• Construct a bar graph of the data
• \(1 - (0.02 + 0.09 + 0.19 + 0.26 + 0.18 + 0.17 + 0.02 + 0.01) = 0.06\)
• \(0.19 + 0.26 + 0.18 = 0.63\)
• Check student’s solution.

80 th percentile will fall between 50,000 and 75,000

Given the following box plot:

• which quarter has the smallest spread of data? What is that spread?
• which quarter has the largest spread of data? What is that spread?
• find the interquartile range ( IQR ).
• are there more data in the interval 5–10 or in the interval 10–13? How do you know this?
• 10–12
• 12–13
• need more information

The following box plot shows the U.S. population for 1990, the latest available year.

• Are there fewer or more children (age 17 and under) than senior citizens (age 65 and over)? How do you know?
• 12.6% are age 65 and over. Approximately what percentage of the population are working age adults (above age 17 to age 65)?
• more children; the left whisker shows that 25% of the population are children 17 and younger. The right whisker shows that 25% of the population are adults 50 and older, so adults 65 and over represent less than 25%.

2.5: Box Plots

In a survey of 20-year-olds in China, Germany, and the United States, people were asked the number of foreign countries they had visited in their lifetime. The following box plots display the results.

• In complete sentences, describe what the shape of each box plot implies about the distribution of the data collected.
• Have more Americans or more Germans surveyed been to over eight foreign countries?
• Compare the three box plots. What do they imply about the foreign travel of 20-year-old residents of the three countries when compared to each other?

Given the following box plot, answer the questions.

• Think of an example (in words) where the data might fit into the above box plot. In 2–5 sentences, write down the example.
• What does it mean to have the first and second quartiles so close together, while the second to third quartiles are far apart?
• Answers will vary. Possible answer: State University conducted a survey to see how involved its students are in community service. The box plot shows the number of community service hours logged by participants over the past year.
• Because the first and second quartiles are close, the data in this quarter is very similar. There is not much variation in the values. The data in the third quarter is much more variable, or spread out. This is clear because the second quartile is so far away from the third quartile.

Given the following box plots, answer the questions.

• Data 1 has more data values above two than Data 2 has above two.
• The data sets cannot have the same mode.
• For Data 1 , there are more data values below four than there are above four.
• For which group, Data 1 or Data 2, is the value of “7” more likely to be an outlier? Explain why in complete sentences.

A survey was conducted of 130 purchasers of new BMW 3 series cars, 130 purchasers of new BMW 5 series cars, and 130 purchasers of new BMW 7 series cars. In it, people were asked the age they were when they purchased their car. The following box plots display the results.

• In complete sentences, describe what the shape of each box plot implies about the distribution of the data collected for that car series.
• Which group is most likely to have an outlier? Explain how you determined that.
• Compare the three box plots. What do they imply about the age of purchasing a BMW from the series when compared to each other?
• Look at the BMW 5 series. Which quarter has the smallest spread of data? What is the spread?
• Look at the BMW 5 series. Which quarter has the largest spread of data? What is the spread?
• Look at the BMW 5 series. Estimate the interquartile range (IQR).
• Look at the BMW 5 series. Are there more data in the interval 31 to 38 or in the interval 45 to 55? How do you know this?
• 31–35
• 38–41
• 41–64
• Each box plot is spread out more in the greater values. Each plot is skewed to the right, so the ages of the top 50% of buyers are more variable than the ages of the lower 50%.
• The BMW 3 series is most likely to have an outlier. It has the longest whisker.
• Comparing the median ages, younger people tend to buy the BMW 3 series, while older people tend to buy the BMW 7 series. However, this is not a rule, because there is so much variability in each data set.
• The second quarter has the smallest spread. There seems to be only a three-year difference between the first quartile and the median.
• The third quarter has the largest spread. There seems to be approximately a 14-year difference between the median and the third quartile.
• IQR ~ 17 years
• There is not enough information to tell. Each interval lies within a quarter, so we cannot tell exactly where the data in that quarter is concentrated.
• The interval from 31 to 35 years has the fewest data values. Twenty-five percent of the values fall in the interval 38 to 41, and 25% fall between 41 and 64. Since 25% of values fall between 31 and 38, we know that fewer than 25% fall between 31 and 35.

Twenty-five randomly selected students were asked the number of movies they watched the previous week. The results are as follows:

Construct a box plot of the data.

2.6: Measures of the Center of the Data

The most obese countries in the world have obesity rates that range from 11.4% to 74.6%. This data is summarized in the following table.

• What is the best estimate of the average obesity percentage for these countries?
• The United States has an average obesity rate of 33.9%. Is this rate above average or below?
• How does the United States compare to other countries?

The table below gives the percent of children under five considered to be underweight. What is the best estimate for the mean percentage of underweight children?

The mean percentage, \(\bar{x} = \frac{1328.65}{50} = 26.75\)

2.7: Skewness and the Mean, Median, and Mode

The median age of the U.S. population in 1980 was 30.0 years. In 1991, the median age was 33.1 years.

• What does it mean for the median age to rise?
• Give two reasons why the median age could rise.
• For the median age to rise, is the actual number of children less in 1991 than it was in 1980? Why or why not?

2.8: Measures of the Spread of the Data

Use the following information to answer the next nine exercises: The population parameters below describe the full-time equivalent number of students (FTES) each year at Lake Tahoe Community College from 1976–1977 through 2004–2005.

• \(\mu = 1000\) FTES
• median = 1,014 FTES
• \(\sigma = 474\) FTES
• first quartile = 528.5 FTES
• third quartile = 1,447.5 FTES
• \(n = 29\) years

A sample of 11 years is taken. About how many are expected to have a FTES of 1014 or above? Explain how you determined your answer.

The median value is the middle value in the ordered list of data values. The median value of a set of 11 will be the 6th number in order. Six years will have totals at or below the median.

75% of all years have an FTES:

• at or below: _____
• at or above: _____

The population standard deviation = _____

What percent of the FTES were from 528.5 to 1447.5? How do you know?

What is the IQR ? What does the IQR represent?

How many standard deviations away from the mean is the median?

Additional Information: The population FTES for 2005–2006 through 2010–2011 was given in an updated report. The data are reported here.

Calculate the mean, median, standard deviation, the first quartile, the third quartile and the IQR . Round to one decimal place.

• mean = 1,809.3
• median = 1,812.5
• standard deviation = 151.2
• first quartile = 1,690
• third quartile = 1,935

Construct a box plot for the FTES for 2005–2006 through 2010–2011 and a box plot for the FTES for 1976–1977 through 2004–2005.

Compare the IQR for the FTES for 1976–77 through 2004–2005 with the IQR for the FTES for 2005-2006 through 2010–2011. Why do you suppose the IQR s are so different?

Hint: Think about the number of years covered by each time period and what happened to higher education during those periods.

Three students were applying to the same graduate school. They came from schools with different grading systems. Which student had the best GPA when compared to other students at his school? Explain how you determined your answer.

A music school has budgeted to purchase three musical instruments. They plan to purchase a piano costing $3,000, a guitar costing $550, and a drum set costing $600. The mean cost for a piano is $4,000 with a standard deviation of $2,500. The mean cost for a guitar is $500 with a standard deviation of $200. The mean cost for drums is $700 with a standard deviation of $100. Which cost is the lowest, when compared to other instruments of the same type? Which cost is the highest when compared to other instruments of the same type. Justify your answer.

For pianos, the cost of the piano is 0.4 standard deviations BELOW the mean. For guitars, the cost of the guitar is 0.25 standard deviations ABOVE the mean. For drums, the cost of the drum set is 1.0 standard deviations BELOW the mean. Of the three, the drums cost the lowest in comparison to the cost of other instruments of the same type. The guitar costs the most in comparison to the cost of other instruments of the same type.

An elementary school class ran one mile with a mean of 11 minutes and a standard deviation of three minutes. Rachel, a student in the class, ran one mile in eight minutes. A junior high school class ran one mile with a mean of nine minutes and a standard deviation of two minutes. Kenji, a student in the class, ran 1 mile in 8.5 minutes. A high school class ran one mile with a mean of seven minutes and a standard deviation of four minutes. Nedda, a student in the class, ran one mile in eight minutes.

• Why is Kenji considered a better runner than Nedda, even though Nedda ran faster than he?
• Who is the fastest runner with respect to his or her class? Explain why.

The most obese countries in the world have obesity rates that range from 11.4% to 74.6%. This data is summarized in the table belo2

What is the best estimate of the average obesity percentage for these countries? What is the standard deviation for the listed obesity rates? The United States has an average obesity rate of 33.9%. Is this rate above average or below? How “unusual” is the United States’ obesity rate compared to the average rate? Explain.

• \(\bar{x} = 23.32\)
• Using the TI 83/84, we obtain a standard deviation of: \(s_{x} = 12.95\).
• The obesity rate of the United States is 10.58% higher than the average obesity rate.
• Since the standard deviation is 12.95, we see that \(23.32 + 12.95 = 36.27\) is the obesity percentage that is one standard deviation from the mean. The United States obesity rate is slightly less than one standard deviation from the mean. Therefore, we can assume that the United States, while 34% obese, does not have an unusually high percentage of obese people.

The Table below gives the percent of children under five considered to be underweight.

What is the best estimate for the mean percentage of underweight children? What is the standard deviation? Which interval(s) could be considered unusual? Explain.

Graphical Representation

Graphical representation definition.

Graphical representation refers to the use of charts and graphs to visually display, analyze, clarify, and interpret numerical data, functions, and other qualitative structures. ‍

What is Graphical Representation?

Graphical representation refers to the use of intuitive charts to clearly visualize and simplify data sets. Data is ingested into graphical representation of data software and then represented by a variety of symbols, such as lines on a line chart, bars on a bar chart, or slices on a pie chart, from which users can gain greater insight than by numerical analysis alone. 

Representational graphics can quickly illustrate general behavior and highlight phenomenons, anomalies, and relationships between data points that may otherwise be overlooked, and may contribute to predictions and better, data-driven decisions. The types of representational graphics used will depend on the type of data being explored.

Types of Graphical Representation

Data charts are available in a wide variety of maps, diagrams, and graphs that typically include textual titles and legends to denote the purpose, measurement units, and variables of the chart. Choosing the most appropriate chart depends on a variety of different factors -- the nature of the data, the purpose of the chart, and whether a graphical representation of qualitative data or a graphical representation of quantitative data is being depicted. There are dozens of different formats for graphical representation of data. Some of the most popular charts include:

• Bar Graph -- contains a vertical axis and horizontal axis and displays data as rectangular bars with lengths proportional to the values that they represent; a useful visual aid for marketing purposes
• Choropleth -- thematic map in which an aggregate summary of a geographic characteristic within an area is represented by patterns of shading proportionate to a statistical variable
• Flow Chart -- diagram that depicts a workflow graphical representation with the use of arrows and geometric shapes; a useful visual aid for business and finance purposes
• Heatmap -- a colored, two-dimensional matrix of cells in which each cell represents a grouping of data and each cell’s color indicates its relative value
• Histogram – frequency distribution and graphical representation uses adjacent vertical bars erected over discrete intervals to represent the data frequency within a given interval; a useful visual aid for meteorology and environment purposes
• Line Graph – displays continuous data; ideal for predicting future events over time;  a useful visual aid for marketing purposes
• Pie Chart -- shows percentage values as a slice of pie; a useful visual aid for marketing purposes
• Pointmap -- CAD & GIS contract mapping and drafting solution that visualizes the location of data on a map by plotting geographic latitude and longitude data
• Scatter plot -- a diagram that shows the relationship between two sets of data, where each dot represents individual pieces of data and each axis represents a quantitative measure
• Stacked Bar Graph -- a graph in which each bar is segmented into parts, with the entire bar representing the whole, and each segment representing different categories of that whole; a useful visual aid for political science and sociology purposes
• Timeline Chart -- a long bar labelled with dates paralleling it that display a list of events in chronological order, a useful visual aid for history charting purposes
• Tree Diagram -- a hierarchical genealogical tree that illustrates a family structure; a useful visual aid for history charting purposes
• Venn Diagram -- consists of multiple overlapping usually circles, each representing a set; the default inner join graphical representation

Proprietary and open source software for graphical representation of data is available in a wide variety of programming languages. Software packages often provide spreadsheets equipped with built-in charting functions.

Advantages and Disadvantages of Graphical Representation of Data

Tabular and graphical representation of data are a vital component in analyzing and understanding large quantities of numerical data and the relationship between data points. Data visualization is one of the most fundamental approaches to data analysis, providing an intuitive and universal means to visualize, abstract, and share complex data patterns. The primary advantages of graphical representation of data are:

• Facilitates and improves learning: graphics make data easy to understand and eliminate language and literacy barriers
• Understanding content: visuals are more effective than text in human understanding
• Flexibility of use: graphical representation can be leveraged in nearly every field involving data
• Increases structured thinking: users can make quick, data-driven decisions at a glance with visual aids
• Supports creative, personalized reports for more engaging and stimulating visual  presentations 
• Improves communication: analyzing graphs that highlight relevant themes is significantly faster than reading through a descriptive report line by line
• Shows the whole picture: an instantaneous, full view of all variables, time frames, data behavior and relationships

Disadvantages of graphical representation of data typically concern the cost of human effort and resources, the process of selecting the most appropriate graphical and tabular representation of data, greater design complexity of visualizing data, and the potential for human bias.

Why Graphical Representation of Data is Important

Graphic visual representation of information is a crucial component in understanding and identifying patterns and trends in the ever increasing flow of data. Graphical representation enables the quick analysis of large amounts of data at one time and can aid in making predictions and informed decisions. Data visualizations also make collaboration significantly more efficient by using familiar visual metaphors to illustrate relationships and highlight meaning, eliminating complex, long-winded explanations of an otherwise chaotic-looking array of figures. 

Data only has value once its significance has been revealed and consumed, and its consumption is best facilitated with graphical representation tools that are designed with human cognition and perception in mind. Human visual processing is very efficient at detecting relationships and changes between sizes, shapes, colors, and quantities. Attempting to gain insight from numerical data alone, especially in big data instances in which there may be billions of rows of data, is exceedingly cumbersome and inefficient.

Does HEAVY.AI Offer a Graphical Representation Solution?

HEAVY.AI's visual analytics platform is an interactive data visualization client that works seamlessly with server-side technologies HEAVY.AIDB and Render to enable data science analysts to easily visualize and instantly interact with massive datasets. Analysts can interact with conventional charts and data tables, as well as big data graphical representations such as massive-scale scatterplots and geo charts. Data visualization contributes to a broad range of use cases, including performance analysis in business and guiding research in academia.

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16 Best Types of Charts and Graphs for Data Visualization [+ Guide]

Published: June 08, 2023

There are more type of charts and graphs than ever before because there's more data. In fact, the volume of data in 2025 will be almost double the data we create, capture, copy, and consume today.

This makes data visualization essential for businesses. Different types of graphs and charts can help you:

• Motivate your team to take action.
• Impress stakeholders with goal progress.
• Show your audience what you value as a business.

Data visualization builds trust and can organize diverse teams around new initiatives. Let's talk about the types of graphs and charts that you can use to grow your business.

Free Excel Graph Templates

Tired of struggling with spreadsheets? These free Microsoft Excel Graph Generator Templates can help.

• Simple, customizable graph designs.
• Data visualization tips & instructions.
• Templates for two, three, four, and five-variable graph templates.

You're all set!

Click this link to access this resource at any time.

Different Types of Graphs for Data Visualization

1. bar graph.

A bar graph should be used to avoid clutter when one data label is long or if you have more than 10 items to compare.

Best Use Cases for These Types of Graphs

Bar graphs can help you compare data between different groups or to track changes over time. Bar graphs are most useful when there are big changes or to show how one group compares against other groups.

The example above compares the number of customers by business role. It makes it easy to see that there is more than twice the number of customers per role for individual contributors than any other group.

A bar graph also makes it easy to see which group of data is highest or most common.

For example, at the start of the pandemic, online businesses saw a big jump in traffic. So, if you want to look at monthly traffic for an online business, a bar graph would make it easy to see that jump.

Other use cases for bar graphs include:

• Product comparisons.
• Product usage.
• Category comparisons.
• Marketing traffic by month or year.
• Marketing conversions.

Design Best Practices for Bar Graphs

• Use consistent colors throughout the chart, selecting accent colors to highlight meaningful data points or changes over time.
• Use horizontal labels to improve readability.
• Start the y-axis at 0 to appropriately reflect the values in your graph.

2. Line Graph

A line graph reveals trends or progress over time, and you can use it to show many different categories of data. You should use it when you chart a continuous data set.

Line graphs help users track changes over short and long periods. Because of this, these types of graphs are good for seeing small changes.

Line graphs can help you compare changes for more than one group over the same period. They're also helpful for measuring how different groups relate to each other.

A business might use this graph to compare sales rates for different products or services over time.

These charts are also helpful for measuring service channel performance. For example, a line graph that tracks how many chats or emails your team responds to per month.

Design Best Practices for Line Graphs

• Use solid lines only.
• Don't plot more than four lines to avoid visual distractions.
• Use the right height so the lines take up roughly 2/3 of the y-axis' height.

3. Bullet Graph

A bullet graph reveals progress towards a goal, compares this to another measure, and provides context in the form of a rating or performance.

In the example above, the bullet graph shows the number of new customers against a set customer goal. Bullet graphs are great for comparing performance against goals like this.

These types of graphs can also help teams assess possible roadblocks because you can analyze data in a tight visual display.

For example, you could create a series of bullet graphs measuring performance against benchmarks or use a single bullet graph to visualize these KPIs against their goals:

• Customer satisfaction.
• Average order size.
• New customers.

Seeing this data at a glance and alongside each other can help teams make quick decisions.

Bullet graphs are one of the best ways to display year-over-year data analysis. You can also use bullet graphs to visualize:

• Customer satisfaction scores.
• Customer shopping habits.
• Social media usage by platform.

Design Best Practices for Bullet Graphs

• Use contrasting colors to highlight how the data is progressing.
• Use one color in different shades to gauge progress.

Different Types of Charts for Data Visualization

To better understand these chart types and how you can use them, here's an overview of each:

1. Column Chart

Use a column chart to show a comparison among different items or to show a comparison of items over time. You could use this format to see the revenue per landing page or customers by close date.

Best Use Cases for This Type of Chart

You can use both column charts and bar graphs to display changes in data, but column charts are best for negative data. The main difference, of course, is that column charts show information vertically while bar graphs show data horizontally.

For example, warehouses often track the number of accidents on the shop floor. When the number of incidents falls below the monthly average, a column chart can make that change easier to see in a presentation.

In the example above, this column chart measures the number of customers by close date. Column charts make it easy to see data changes over a period of time. This means that they have many use cases, including:

• Customer survey data, like showing how many customers prefer a specific product or how much a customer uses a product each day.
• Sales volume, like showing which services are the top sellers each month or the number of sales per week.
• Profit and loss, showing where business investments are growing or falling.

Design Best Practices for Column Charts

2. dual-axis chart.

A dual-axis chart allows you to plot data using two y-axes and a shared x-axis. It has three data sets. One is a continuous data set, and the other is better suited to grouping by category. Use this chart to visualize a correlation or the lack thereof between these three data sets.

A dual-axis chart makes it easy to see relationships between different data sets. They can also help with comparing trends.

For example, the chart above shows how many new customers this company brings in each month. It also shows how much revenue those customers are bringing the company.

This makes it simple to see the connection between the number of customers and increased revenue.

You can use dual-axis charts to compare:

• Price and volume of your products.
• Revenue and units sold.
• Sales and profit margin.
• Individual sales performance.

Design Best Practices for Dual-Axis Charts

• Use the y-axis on the left side for the primary variable because brains naturally look left first.
• Use different graphing styles to illustrate the two data sets, as illustrated above.
• Choose contrasting colors for the two data sets.

3. Area Chart

An area chart is basically a line chart, but the space between the x-axis and the line is filled with a color or pattern. It is useful for showing part-to-whole relations, like showing individual sales reps’ contributions to total sales for a year. It helps you analyze both overall and individual trend information.

Best Use Cases for These Types of Charts

Area charts help show changes over time. They work best for big differences between data sets and help visualize big trends.

For example, the chart above shows users by creation date and life cycle stage.

A line chart could show more subscribers than marketing qualified leads. But this area chart emphasizes how much bigger the number of subscribers is than any other group.

These charts make the size of a group and how groups relate to each other more visually important than data changes over time.

Area graphs can help your business to:

• Visualize which product categories or products within a category are most popular.
• Show key performance indicator (KPI) goals vs. outcomes.
• Spot and analyze industry trends.

Design Best Practices for Area Charts

• Use transparent colors so information isn't obscured in the background.
• Don't display more than four categories to avoid clutter.
• Organize highly variable data at the top of the chart to make it easy to read.

4. Stacked Bar Chart

Use this chart to compare many different items and show the composition of each item you’re comparing.

These graphs are helpful when a group starts in one column and moves to another over time.

For example, the difference between a marketing qualified lead (MQL) and a sales qualified lead (SQL) is sometimes hard to see. The chart above helps stakeholders see these two lead types from a single point of view — when a lead changes from MQL to SQL.

Stacked bar charts are excellent for marketing. They make it simple to add a lot of data on a single chart or to make a point with limited space.

These graphs can show multiple takeaways, so they're also super for quarterly meetings when you have a lot to say but not a lot of time to say it.

Stacked bar charts are also a smart option for planning or strategy meetings. This is because these charts can show a lot of information at once, but they also make it easy to focus on one stack at a time or move data as needed.

You can also use these charts to:

• Show the frequency of survey responses.
• Identify outliers in historical data.
• Compare a part of a strategy to its performance as a whole.

Design Best Practices for Stacked Bar Graphs

• Best used to illustrate part-to-whole relationships.
• Use contrasting colors for greater clarity.
• Make the chart scale large enough to view group sizes in relation to one another.

5. Mekko Chart

Also known as a Marimekko chart, this type of graph can compare values, measure each one's composition, and show data distribution across each one.

It's similar to a stacked bar, except the Mekko's x-axis can capture another dimension of your values — instead of time progression, like column charts often do. In the graphic below, the x-axis compares the cities to one another.

Image Source

You can use a Mekko chart to show growth, market share, or competitor analysis.

For example, the Mekko chart above shows the market share of asset managers grouped by location and the value of their assets. This chart clarifies which firms manage the most assets in different areas.

It's also easy to see which asset managers are the largest and how they relate to each other.

Mekko charts can seem more complex than other types of charts and graphs, so it's best to use these in situations where you want to emphasize scale or differences between groups of data.

Other use cases for Mekko charts include:

• Detailed profit and loss statements.
• Revenue by brand and region.
• Product profitability.
• Share of voice by industry or niche.

Design Best Practices for Mekko Charts

• Vary your bar heights if the portion size is an important point of comparison.
• Don't include too many composite values within each bar. Consider reevaluating your presentation if you have a lot of data.
• Order your bars from left to right in such a way that exposes a relevant trend or message.

6. Pie Chart

A pie chart shows a static number and how categories represent part of a whole — the composition of something. A pie chart represents numbers in percentages, and the total sum of all segments needs to equal 100%.

The image above shows another example of customers by role in the company.

The bar graph example shows you that there are more individual contributors than any other role. But this pie chart makes it clear that they make up over 50% of customer roles.

Pie charts make it easy to see a section in relation to the whole, so they are good for showing:

• Customer personas in relation to all customers.
• Revenue from your most popular products or product types in relation to all product sales.
• Percent of total profit from different store locations.

Design Best Practices for Pie Charts

• Don't illustrate too many categories to ensure differentiation between slices.
• Ensure that the slice values add up to 100%.
• Order slices according to their size.

7. Scatter Plot Chart

A scatter plot or scattergram chart will show the relationship between two different variables or reveal distribution trends.

Use this chart when there are many different data points, and you want to highlight similarities in the data set. This is useful when looking for outliers or understanding your data's distribution.

Scatter plots are helpful in situations where you have too much data to see a pattern quickly. They are best when you use them to show relationships between two large data sets.

In the example above, this chart shows how customer happiness relates to the time it takes for them to get a response.

This type of graph makes it easy to compare two data sets. Use cases might include:

• Employment and manufacturing output.
• Retail sales and inflation.
• Visitor numbers and outdoor temperature.
• Sales growth and tax laws.

Try to choose two data sets that already have a positive or negative relationship. That said, this type of graph can also make it easier to see data that falls outside of normal patterns.

Design Best Practices for Scatter Plots

• Include more variables, like different sizes, to incorporate more data.
• Start the y-axis at 0 to represent data accurately.
• If you use trend lines, only use a maximum of two to make your plot easy to understand.

8. Bubble Chart

A bubble chart is similar to a scatter plot in that it can show distribution or relationship. There is a third data set shown by the size of the bubble or circle.

In the example above, the number of hours spent online isn't just compared to the user's age, as it would be on a scatter plot chart.

Instead, you can also see how the gender of the user impacts time spent online.

This makes bubble charts useful for seeing the rise or fall of trends over time. It also lets you add another option when you're trying to understand relationships between different segments or categories.

For example, if you want to launch a new product, this chart could help you quickly see your new product's cost, risk, and value. This can help you focus your energies on a low-risk new product with a high potential return.

You can also use bubble charts for:

• Top sales by month and location.
• Customer satisfaction surveys.
• Store performance tracking.
• Marketing campaign reviews.

Design Best Practices for Bubble Charts

• Scale bubbles according to area, not diameter.
• Make sure labels are clear and visible.
• Use circular shapes only.

9. Waterfall Chart

Use a waterfall chart to show how an initial value changes with intermediate values — either positive or negative — and results in a final value.

Use this chart to reveal the composition of a number. An example of this would be to showcase how different departments influence overall company revenue and lead to a specific profit number.

The most common use case for a funnel chart is the marketing or sales funnel. But there are many other ways to use this versatile chart.

If you have at least four stages of sequential data, this chart can help you easily see what inputs or outputs impact the final results.

For example, a funnel chart can help you see how to improve your buyer journey or shopping cart workflow. This is because it can help pinpoint major drop-off points.

Other stellar options for these types of charts include:

• Deal pipelines.
• Conversion and retention analysis.
• Bottlenecks in manufacturing and other multi-step processes.
• Marketing campaign performance.
• Website conversion tracking.

Design Best Practices for Funnel Charts

• Scale the size of each section to accurately reflect the size of the data set.
• Use contrasting colors or one color in graduated hues, from darkest to lightest, as the size of the funnel decreases.

11. Heat Map

A heat map shows the relationship between two items and provides rating information, such as high to low or poor to excellent. This chart displays the rating information using varying colors or saturation.

Best Use Cases for Heat Maps

In the example above, the darker the shade of green shows where the majority of people agree.

With enough data, heat maps can make a viewpoint that might seem subjective more concrete. This makes it easier for a business to act on customer sentiment.

There are many uses for these types of charts. In fact, many tech companies use heat map tools to gauge user experience for apps, online tools, and website design .

Another common use for heat map graphs is location assessment. If you're trying to find the right location for your new store, these maps can give you an idea of what the area is like in ways that a visit can't communicate.

Heat maps can also help with spotting patterns, so they're good for analyzing trends that change quickly, like ad conversions. They can also help with:

• Competitor research.
• Customer sentiment.
• Sales outreach.
• Campaign impact.
• Customer demographics.

Design Best Practices for Heat Map

• Use a basic and clear map outline to avoid distracting from the data.
• Use a single color in varying shades to show changes in data.
• Avoid using multiple patterns.

12. Gantt Chart

The Gantt chart is a horizontal chart that dates back to 1917. This chart maps the different tasks completed over a period of time.

Gantt charting is one of the most essential tools for project managers. It brings all the completed and uncompleted tasks into one place and tracks the progress of each.

While the left side of the chart displays all the tasks, the right side shows the progress and schedule for each of these tasks.

This chart type allows you to:

• Break projects into tasks.
• Track the start and end of the tasks.
• Set important events, meetings, and announcements.
• Assign tasks to the team and individuals.

Download the Excel templates mentioned in the video here.

5 Questions to Ask When Deciding Which Type of Chart to Use

1. do you want to compare values.

Charts and graphs are perfect for comparing one or many value sets, and they can easily show the low and high values in the data sets. To create a comparison chart, use these types of graphs:

• Scatter plot

2. Do you want to show the composition of something?

Use this type of chart to show how individual parts make up the whole of something, like the device type used for mobile visitors to your website or total sales broken down by sales rep.

To show composition, use these charts:

• Stacked bar

3. Do you want to understand the distribution of your data?

Distribution charts help you to understand outliers, the normal tendency, and the range of information in your values.

Use these charts to show distribution:

4. Are you interested in analyzing trends in your data set?

If you want more information about how a data set performed during a specific time, there are specific chart types that do extremely well.

You should choose one of the following:

• Dual-axis line

5. Do you want to better understand the relationship between value sets?

Relationship charts can show how one variable relates to one or many different variables. You could use this to show how something positively affects, has no effect, or negatively affects another variable.

When trying to establish the relationship between things, use these charts:

Featured Resource: The Marketer's Guide to Data Visualization

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10 Good and Bad Examples of Data Visualization in 2024

Make sure you avoid these common mistakes when visualizing data as well as some best practices to follow in 2024

As someone who’s been doing data visualization for over 10 years, I’ve come across so many mistakes people make. Make sure you avoid these common mistakes when visualizing data as well as some best practices to follow in 2024. The most common errors include:

• Using the wrong graphs/charts for their particular purpose
• Not making the best use out of colors.
• Creating misleading graphs/charts
• Trying to incorporate too much information in one graph

Whether you're building a dashboard , presentation, or more, it's important to be wise with your data visualization choices.

Here are some examples of each so you can learn to avoid them.

Using the Wrong Graphs and Carts

“Which graphs should I be using?”

As a general rule of thumb:

• Bar charts are for showing the relationship between 1 categorical variable (e.g. color, car model, gender) against 1 numerical variable (height, test scores, IQ and other measurements).
• Pie charts are for the same thing, but aren’t very good for data that contains more than 2-3 categories. E.g. it’s fine for gender since it only has male, female and other, but it’s terrible for listing all car models.
• Scatterplots are for finding correlations between 2 numerical variables.
• Time series are for showing changes over time ( time vs. numerical variable ).

There’s many more than this, but those are the main ones. To learn about this in greater detail, read our post about data visualization techniques .

Bad Data Visualization Example #1: Presenting Qualitative Data

Not all data can be visualized into graphs or charts. For instance, data pertaining to employee details: including first & last name, email address, ethnicity, job title etc.

The biggest mistake would be to present the raw data like this:

Just because a dataset contains a bunch of qualitative data like "name" and "email address" doesn't mean it can't be visualized.

There are two ways to visualize it:

• Gallery View

Card view is good for visualizing raw data:

Gallery view is good for visualizing data with images (for instance: employee headshot photos). An example of gallery view is FlixGem .

Both of these visualizations aren't just to make things "look nicer." But they allow you to easily filter through the data with interactive tags. This is important for both data analysis and presentation.

How to create visualizations for qualitative data:

It might look complicated to create, but existing tools make your job dead simple:

• Upload your data to Polymer Search .
• Launch Polymer App
• Choose the desired layout: Grid view, card view or gallery view

Bad Data Visualization Example #2: Pie chart with too many categories

Pie charts are best used when there are 2-3 items that make up a whole. Any more than that, and it’s difficult for the human eye to distinguish between the parts of a circle.

Notice how it’s hard to distinguish the size of these parts. 

Is “China” bigger than “Other”? 

It’s hard for our eyes to tell the difference. Instead, replace this with a bar chart:

Good example: Proper Bar Chart

Notice how “China” and “Other” are far apart, but we can easily distinguish that one is larger than the other? That’s because our eyes are more sensitive to length of bars than parts of a circle. 

Bar charts will be your go-to chart for data visualization.

Bad Data Visualization Example #3: Multi-colored bar charts

It might look pretty, and you might be wondering “what’s wrong with it?”

The more colors you use, the less comprehensible the visualization will be. More colors = more categories the brain must process. 

On top of that, there’s a better way to handle colors:

Good example: Proper color design

Colors allow us to highlight whatever information we want. 

If we wanted to highlight the country with the biggest CO2 emissions, we can use red vs. grey:

Notice how China immediately sticks out and we get the point across.

Other times, it’s a good idea to use multiple shades of the same color.

Another good example: Proper pie chart  

Bad Data Visualization Example #4: Horizontal bar charts

Horizontal bar charts suffer from the same issue as pie charts: once there are too many categories, you run out of space to include text and it becomes hard to digest:

Instead, it’s better to use vertical bar charts (by switching the axes around):

Good example: Vertical bar charts

This gives unlimited space for including text and is easier for the brain to digest.

Bad Data Visualization Example #5: Too much information

Here’s an example of someone trying to include too much information on one chart: 

Including too much information ruins the point of data visualization in the first place. The purpose of data visualization is to allow the audience to easily digest the information and this graph does the opposite of that.

Instead, take the time to rearrange your data and create multiple graphs to convey your point.

Bad Data Visualization Example #6: 3D graphs

Studies have shown that 3D rendering can negatively affect graph comprehension. It might be tempting to be creative and ‘3D’ your graphs, but there are better ways to get creative .

Bad Data Visualization Example #7: Charts that don’t start at zero (misleading)

Sometimes it’s okay to break this rule, but in general:

• Bar charts should always start at 0, because our eyes are very sensitive to the size of bars.
• Scatterplots and time series should almost always start at 0.
• Line graphs can sometimes break this rule.

Since the y-axis doesn’t start at 0, it’s easy to fool someone that product 2 is failing, but in actuality:

The same applies to other graphs like time series:

Since the y-axis doesn’t start at 0, it’s easy to fool someone that the price of something is exponentially rising where in actuality, the increase is only about 10-20%.

Bad Data Visualization Example #8: Tables With no Context

Spreadsheets and pivot tables with no context are meaningless.

Look at this pivot table:

It's a pivot table showing which product line and gender are generating the most income. Even though it's ordered from highest to lowest income generated, what exactly do these numbers mean? How high is $1580?

These numbers are meaningless without context.

Good Example: Table with Context

Instead of just giving a raw number, it's highly recommended to provide a mean deviation, that is, how far a number is from the average:

Now we can look and go "Oh $1580 is 23% above the mean."

Creating these might be off-putting to some people since it takes more time and effort, but a tool like Polymer Search does all of this automatically for you - and creates pivot tables faster than Excel .

Bad Data Visualization Example #9: Too much graphics, no structure

The aesthetic aspect of data visualizations is undoubtedly important. But it should not come at the expense of digestibility. 

Take the graphic below, for example. 

There's no doubt that the artist spent a lot of time creating this piece. But there are a handful of issues that leave much to be desired.

For one, the visualization follows no organizational structure or order whatsoever. 

At first look, users may think that the headers in each "slice" are the names of the apps that produced the data. In turn, some people might think there's an app out there called "#LOVE" or "Americans." 

Some slices don't even have a header at all. Only upon close inspection, which takes a couple of minutes of the audience's precious time, will they realize that these headers aren't what they seem. 

The visualization also uses some questionable color pairs (just take a look at the "Airbnb" and "Twitter" sections). 

However, out of all these issues, the biggest problem is that the creators decided to build a single graphic for multiple, inconsistent data types. 

Remember, popular data visualization formats like pie charts, graphs, and tables exist for a reason — and that is to help readers comprehend data faster and more effectively. For that to work, you need to start with a consistent, clean dataset that comes together to tell a cohesive story.

If the graphic above is meant to bombard the user with a mash of large numbers, you can say that the creators succeeded. 

Bad Data Visualization Example #10: Uncalibrated Y-Axis

In data visualizations, a single setting can substantially change the way users interpret the data. 

Charts that don't start at zero are a great example, and they are often used to mislead the audience. 

Another example is the chart below:

You might think that interest rates soared from 2008 to 2012. After all, the bar for 2012 is several times higher compared to the one for 2008. 

But if you read the scale, you'll know that interest rates actually only increased by a tiny 0.012% in four years. 

Good example: Chart with a reasonable Y-Axis range

The bar chart above is created to highlight the importance of the Y-Axis. By using an extremely minuscule range, the differences between the numbers are greatly exaggerated. 

Here's the same data with a more reasonable Y-Axis range:

Bad Data Visualization Example #11: Unproportionate pie slices

Pie charts with too many categories are bad, but at least they aren't deliberately misleading. 

Charts that flat-out misrepresent the numbers with unproportionate visuals, however, are a serious offense. 

Take a look at this chart, for example:

The visual may look cute, but the numbers don't make sense. 

For instance, the 38.5% slice is roughly twice the size of the 31.0% part. It's also misleading that the 31%, 17.1%, and 7.2% slices are very similar in size. 

If we were to guess, the creator may have built the data visualization manually and failed to use proportional sizes for the data. 

Good example: Tool-based chart with accurate proportions

With a data visualization tool, you don't have to second-guess the graphical proportions of your data. You simply choose a data visualization type, plug in the numbers, and watch the software render the graphic for you.

Granted, this version doesn't have bees and fancy shading. But it does a much better job of communicating the accurate and proportionate composition of honey.

Not to mention that it took less than five minutes to create this graphic using a data visualization tool. 

Another good example: Auto-generated pie chart

Here's another good example, which is an automatically generated pie chart using pre-loaded values on Polymer:

This time, the graphic only took a few seconds to create. That's because Polymer's drag-and-drop visualization tool instantly creates anything — from column charts to pivot tables — using values from a connected data source.

Bad Data Visualization Example #12: Truncated Y-Axis

You've already seen Y-Axes that don't start at 0 and use uncalibrated ranges. 

The chart below uses a different tactic that can skew how viewers interpret the data. 

First off, notice that the "government funding" bar is smaller than the "revenue" bar in both years. That's despite the fact that the government funding in both years exceeded $1.2 billion, whereas the revenue bars are only supposed to represent $490 million and $573 million.

Misleading, right? 

Technically, the visualization didn't lie. The problem is, the Y-Axis scale is truncated from $700 million all the way to $1.7 billion (that's $1 billion jump). 

While the chart itself is correct within the scale, the gap between $700 million and $1.7 billion made the "government funding" bar a lot smaller than it is. 

Now, we're in no position to claim whether this is intentional or not. But, if you're creating charts, never use a truncated Y-Axis to prevent viewers from misinterpreting your data.

Good example: Clean bar chart with a consistent scale

Whether you're creating a horizontal or vertical bar chart, never mess with your scale. 

In the example below, we used Polymer to visualize the total number of leads generated per keyword in a Pay Per Click (PPC) campaign:

Notice how the X-Axis scale starts at 0 and uses a consistent, reasonable range. This gives a much clearer picture of how much better "bubble inventory download" is than other keywords in terms of generating leads. 

Bad Data Visualization Example #13: Unique for no reason

This next data visualization is pretty interesting — and not in a good way. 

As you can see, the graphic is meant to visualize the frequency of consumers doing gardening work. 

There's absolutely no reason for this not to be a bar or pie chart. But instead of using a popular, more readable type of visualization, the creator decided to use a unique graphic for no good reason. 

Another problem is the header "less often." 

Less often than what? 

Bear in mind that the visualization is based on a survey. That means people either had to respond with "less often" or something else more meaningful, like "less than once a month." 

If it's the latter, why didn't the visualization just say that?

Good example: Keeping it simple

If you think about it, the story behind the visualization above is rather straightforward. It's just a survey on how often consumers do gardening work — and the job is simply to visualize the results and make the data easier to digest. 

Here's an efficient way to do this: 

Apart from using a crystal-clear bar chart, the label "less often" is also replaced with "less than once a month." This eliminates any ambiguity from the visualization's message.

Bad Data Visualization Example #14: Needlessly 3D chart

3D and data simply don't mix — as mentioned in #6 above. 

But the NYTimes back in 2008 decided that 3D rendering doesn't make a chart confusing enough. 

Can you guess what type of chart is actually being used here? 

If you guessed "bar chart," congratulations — you're just like most people. 

But most people guessed wrong. 

In fact, the chart above is a list of several pie charts. 

Look closely and notice how the charts bend inwards in the middle. That's the center point of the pie chart. 

Here's the thing: the data can be adequately explained with either a bar chart or a collection of pie charts. Even a simple bulleted list would've effectively conveyed the message. 

The problem is, they used 3D pie charts and tried to arrange them like a typical bar graph. It's one bad design choice on top of another. 

Good example: Clean, readable, and interactive table

This Polymer-powered table offers the fastest and most efficient way to convey the information above:

Not only is the visualization clean and readable, but it's also interactive. 

Users can filter out professions they're not interested in reviewing. Additionally, the data can be sorted alphabetically or based on their prestige rating.

Another good example: Interactive bar graph

Here's the same data represented as an interactive bar graph:

Unlike the original chart from the NYTimes, this graph features plain, flat bars with a value at the center. Everything is the same shade, but the graph still does an immensely better job of conveying information.

Bad Data Visualization Example #15: What's going on here?

Before you look at the next visualization, take a deep breath and prepare for a little headache.

So, what do you think is going on here?

The funny thing is, data visualizations are tools to ease the interpretation of data. They're not supposed to be brain teasers that leave viewers with more questions than when they started. 

It might take you a while to piece this puzzle together. To save you the trouble, here's what's happening in the graphic above. 

The full green bar visualizes the "business should take responsibility" value. That's the percentage of respondents who believe the companies should be responsible for the items listed on the left (create jobs, drive innovation, support local communities, etc.).   

The blue line in the middle marks the actual performance of the business in each expected responsibility. For example, when it comes to creating jobs, 50% of businesses are actually doing well. 

Lastly, the shaded part of the bar to the right is the performance gap. In simple terms, that's the difference between the performance of businesses and the expectations of the respondents. 

Good example: Comparing data side-by-side

Ultimately, the goal of the visualization above is to compare the public's expectations with the actual performance of businesses when it comes to specific issues. 

Rather than stacking both metrics in one bar, just create two bars for each. 

That's why this version is far superior to the mess above:

Bad Data Visualization Example #16: Cumulative charts

A cumulative chart has its uses, but it can be misleading when measuring certain values. 

For example, check out this cumulative annual revenue chart:

The line is going up so the business must be doing well, right? 

Keep in mind that cumulative charts can only go up. If you look really closely, you'll notice an almost imperceptible slope that indicates a slowdown. 

Good example: Year-Over-Year (YOY) charts

Instead of cumulative data, this chart tracks the YOY revenue of the business:

If you're the business owner, this visualization is scary — the complete opposite of what the cumulative chart makes you feel. 

Regardless, the YOY is the visualization you need to see. It shows the reality that your business could be well on its way to shutting down, giving you the opportunity to diagnose the problem and possibly turn things around. 

Bad Data Visualization Example #17: Unadjusted data

You might think that using raw, unadjusted data leads to accurate analyses.

While it's true in some cases, especially with smaller datasets, "unadjusted" means you're completely ignoring the importance of data quality.

In other words, you might end up with a chart like this: 

The chart above is meant to represent yearly recorded temperatures. But since the creator carpet-bombed the plot area with blue dots, it's impossible to use the data for insights. 

Good example: Temperature anomalies by season

There's a reason why data cleansing is important. 

Biases, errors, outliers, and incomplete data can all lead to faulty readings. For one, the chart didn't organize the data by season — a huge factor that affects temperature records.

How can you tell if the dots above 82 degrees aren't due to heat waves (which are, by definition, abnormal)? Should viewers just assume those values were logged during summertime? 

That's why this chart, which segments the data by season, is a lot better in helping the audience understand what the data is trying to say. 

Bad Data Visualization Example #18: Just use the right data visualization type

A lot of data visualization mishaps could've been avoided if the creator only used the appropriate chart type. 

Flexible data visualization and Business Intelligence (BI) tools like Polymer are available. There's no need to forcefully use a sankey chart for a dataset that's best visualized as a table, bar graph, or even a set of pie charts. 

To give you an idea, here's a quick look at the data visualizations you can add to your Polymer dashboard. 

Every visualization tool you need for just about any dataset is here. 

You have zero reason to experiment with an incompatible chart and end up with something awkward or borderline unreadable.

Good Data Visualization Examples

Data visualization can be incredibly powerful when done correctly. Not only does it make complex data more understandable, but it can also reveal patterns, correlations, and insights that might not be visible otherwise. Here are a few examples of good data visualization practices:

• Interactive Dashboards : With advancements in technology, it's now possible to create dynamic and interactive dashboards. These allow users to drill down into specific data points, zoom into regions of interest, or toggle between different data sets. This gives users a more immersive experience and helps them understand the data at a deeper level.
• Use of Color : Color can be a powerful tool when visualizing data. For instance, a heat map that uses a gradient of colors to show the density of data points can quickly highlight areas of interest or concern. But remember, always consider those who are color-blind and choose palettes that are universally understandable.
• Storytelling with Data : Instead of simply presenting numbers and charts, some visualizations tell a story. This can be a sequence of events, a comparison of scenarios, or even a progression over time. When data tells a story, it becomes much more memorable and impactful.

Bad Data Visualization Examples

While there are many ways to effectively visualize data, there are also common pitfalls that can render a visualization confusing or even misleading. Here are some examples of what to avoid:

• 3D Pie Charts : While they might look fancy, 3D pie charts can distort the perception of data. The angles and perspective can make some sections look larger than they actually are, leading to misinterpretations.
• Overloading with Data : It's tempting to include as much data as possible in a single visualization, but this can lead to clutter and confusion. It's crucial to be selective and only include the most relevant data points.
• Ignoring Scale : Not starting the y-axis at zero or using inconsistent scales can make differences seem more pronounced than they actually are. This can be misleading and should be avoided.

In the context of the current content, it's also essential to emphasize the importance of choosing the right type of chart or graph for the data. For example, while bar charts and pie charts have their place, they aren't always the best choice. It's crucial to understand the nature of the data and the message you want to convey before selecting a visualization method.

Once you learn the many data visualization techniques, know when to use each graph and become aware of all the good and bad practices, you’ll be a pro data analyst in no time!

There are many ways to enhance your visualization skills - with Polymer Search you’ll be able to instantly generate interactive graphs/charts/pivot tables in a matter of seconds. You simply upload your data and the AI will automatically turn it into an interactive spreadsheet and provide quick and easy data visualization tools that are available in no other tool.

You’ll also be able to create your own web app in a couple of minutes with no coding experience required. Simply upload your dataset and Polymer will automatically transform it into a web application where you can share all your visualizations. Unleash Polymer's AI  to unlock more insights in your data.

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Bad Data Visualization: 5 Examples of Misleading Data

• 28 Jan 2021

Data visualization , the process of creating visual representations of data, offers businesses various benefits. One of the most powerful is the ability to communicate with data to a wider audience both internally and externally. This, in turn, enables more stakeholders to make data-driven decisions .

In recent years, a proliferation of data visualization tools has made it easier than ever for professionals without a data background to create graphics and charts.

While this has primarily been a positive development, it’s likely given rise to one specific problem: poor or otherwise inaccurate data visualization. Just as well-crafted data visualizations benefit organizations that generate and use them, poorly crafted ones create several problems.

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Data Visualization Mistakes to Avoid

When generating data visualizations, it can be easy to make mistakes that lead to faulty interpretation, especially if you’re just starting out. Below are five common mistakes you should be aware of and some examples that illustrate them.

1. Using the Wrong Type of Chart or Graph

There are many types of charts or graphs you can leverage to represent data visually. This is largely beneficial because it allows you to include some variety in your data visualizations. It can, however, prove detrimental if you choose a graph that isn’t well suited to the insights you’re trying to illustrate.

Some graphs and charts work well for communicating specific types of information, but not others. Problems can arise when you try visualizing data using an unsuitable format.

The nature of your data usually dictates the format of your visualization. The most important characteristic is whether the data is qualitative (it describes or categorizes) or quantitative (meaning, it’s measurable). Qualitative data tends to be better suited to bar graphs and pie charts, while quantitative data is best represented in formats like charts and histograms.

2. Including Too Many Variables

The point of generating a data visualization is to tell a story. As such, it’s your job to include as much relevant information as possible—while excluding irrelevant or unnecessary details. Doing so ensures your audience pays attention to the most important data.

For this reason, in conceptualizing your data visualization, you should first seek to identify the necessary variables. The number of variables you select will then inform your visualization’s format. Ask yourself: Which format will help communicate the data in the clearest manner possible?

A pie chart that compares too many variables, for example, will likely make it difficult to see the differences between values. It might also distract the viewer from the point you’re trying to make.

3. Using Inconsistent Scales

If your chart or graph is meant to show the difference between data points, your scale must remain consistent. If your visualization’s scale is inconsistent, it can cause significant confusion for the viewer.

For example, if you generate a pictogram that uses images to represent a measure of data within a bar graph, the images should remain the same size from column to column.

4. Unclear Linear vs. Logarithmic Scaling

The easiest way to understand the difference between a linear scale and a logarithmic one is to look at the axes that each is built on. When a chart is built on a linear scale, the value between any two points along either axis is always equal and unchanging. When a chart is built on a logarithmic scale, the value between any two points along either axis changes according to a particular pattern.

While logarithmic scaling can be an effective means of communicating data, it must be clear that it’s being used in the graphic. When this is unclear, the viewer may, by default, assume they’re looking at a linear scale, which is more common. This can cause confusion and understate your data’s significance.

For example, the two graphics below communicate the same data. The primary difference is that the graphic on the left is built on a linear scale, while the one on the right is built on a logarithmic one.

This isn’t to say logarithmic scaling shouldn’t be used; simply that, when it’s used, it must be clearly stated and communicated to the viewer.

5. Poor Color Choices

Used carefully, color can make it easier for the viewer to understand the data you’re trying to communicate. When used incorrectly, however, it can cause significant confusion. It’s important to understand the story you’re hoping to tell with your data visualization and choose your colors wisely.

Some common issues that arise when incorporating color into your visualizations include:

• Using too many colors, making it difficult for the reader to quickly understand what they’re looking at
• Using familiar colors (for example, red and green) in surprising ways
• Using colors with little contrast
• Not accounting for viewers who may be colorblind

Consider a bar graph that’s meant to show changes in a technology’s adoption rate. Some of the bars indicate increases in adoption, while others indicate decreases. If you use red to represent increases and green to indicate decreases, it might confuse the viewer, who’s likely accustomed to red meaning negative and green meaning positive.

As another example, consider a US map chart that shows virus infection rates from state to state, with colors representing different concentrations of positive cases. Typically, map charts leverage different shades within the same color family. The lighter the shade, the fewer the cases in that state; the darker the shade, the more cases there are. If you go against this assumption and use a darker color to indicate fewer cases, it could confuse the viewer.

The Impact of Poor Data Visualization

While an inaccurate chart may seem like a small error in the grand scheme of your organization, it can have profound repercussions.

By their nature, inaccurate data visualizations lead your audience to have an inaccurate understanding of the data that’s presented in them. This misunderstanding can lead to faulty insights and poor business decisions—all under the guise that they’re backed by data.

Taken to the extreme, a chart or graph that’s improperly formatted could lead to legal or regulatory issues. For example, a misleading data visualization included in a financial report could cause investors to buy or sell shares of company stock.

For these reasons, a firm understanding of data science is an essential skill for professionals. Knowing when data is accurate and complete, and being able to identify discrepancies between numbers and any visualizations created from them, is a must-have in today’s business environment. An online course centered around business analytics can be an effective way to build these skills .

Do you want to further your data literacy? Download our Beginner’s Guide to Data & Analytics to find out how you can leverage data for professional and organizational success.

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Graphical Representation of Data

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Graphical Representation of Data| Practical Work in Geography Class 12

Geography Class 12 Chapter 3 talks about the Graphical Representation of Data. It includes all types of representation processes of data through different types of graphs like line, bar, pie, dot, and isopleth maps. Graphical representation gives us a visual of the raw data which helps us to understand to analyze it through different numeric formations.

In this article, we are going to discuss the Practical Work in Geography Class 12 with the Chapter called Graphical Representation of Data.

Graphical Representation of Data means to analyze the numerical data sources through different types of graphs. It creates a relation between the data set with a diagram. The graphical representation is simple and easy to understand which is a part of the important learning technique. The Graphical Representation process is totally dependent process on data sources.

Let us discuss different types of Graphical Representation of Data as mentioned below.

• Line Graphs: Line graphs are one type of linear graph that examines the continuous data sources to predict the future.
• Histograms: The histograms use the bar formations to represent the data as the frequency of the numerical data sources. Here the intervals are present in an equal manner.
• Bar Graphs: The bar graphs are used to depict the different categories and compare the data by using solid bars of quantities.
• Frequency Table: The frequency table represents the data by following a proper time interval.
• Line Plot: The line plot shows the data in a manner of frequency that is written as a line number.
• Circle Graph: The circle graph is also known as the pie chart. It shows the relationships between the data parts. The circle holds 100 % data by mentioning the data portions in percentages.
• Scatter Plots: The scatter plots depict the data to establish the relationship between two data sets.
• Venn Diagram: The Venn diagram is a process graph where the set is important. The inner part of the circle makes and shows the representation of graphs.
• Stem and Leaf Plots: They are the representation of the least and highest value of a particular data set. The lowest value is known as the leaf and the highest value is the stem.
• Box and Whisker Plot: This is the process of summarizing the data into four different parts. It majorly represents the spread and median of the different data sets.

Rules for Graphical Representation of Data

There are some major rules to make the Graphical Representation of Data as mentioned below.

• Give a Title: Give a suitable title for the graph which presents the subject.
• Scale: The scale needs to be used efficiently in an accurate way.
• Mention the Measurement Units: It is important to mention the measurement units to represent the dataset as a graph in a proper way.
• Index Formation: It is needed to apply the different types of colors, shades, and designs to make the related graph more understanding with more information.
• Data Sources: Include the proper sources of data at the bottom of the graph when it is necessary to make it more authentic.
• Make It Simple: A simple graph is more understandable than a hard one. You need to make it more easy for the readers.

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Graphical Representation of Data- FAQs

What are the 4 different types of graphical representation.

There are four most widely used graphs namely histogram, pie diagram, frequency polygon, and ogive frequency graph.

What is a graphical form of representation?

Graphical Representation is a way of analysing numerical data. It exhibits the relation between data, ideas, information and concepts in a diagram. It is easy to understand and it is one of the most important learning strategies. It always depends on the type of information in a particular domain.

What are the graphical displays of data?

Two common types of graphic displays are bar charts and histograms. Both bar charts and histograms use vertical or horizontal bars to represent the number of data points in each category or interval.

What is a graphical display?

Graphical displays communicate comparisons, relationships, and trends. They emphasize and clarify numbers. To choose the appropriate type of display, first define the purpose of the report, and then identify the most effective display to suit that purpose. For example, you can use a multiline display to show trends.

What is a graphical representation called?

The method of presenting these numerical data is called a chart. There are different kinds of charts such as a pie chart, bar graph, line graph, etc, that help in clearly showcasing the data.

Why are statistical graphs important?

Raw data might contain hidden patterns and relationships that you cannot identify by just looking at the raw data. These will be revealed using a picture. A display of data will help you identify the most significant features of your data.

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