## Advantages and Disadvantages of Graphical Representation of Data

The graphical view is vastly used in every type of data or report. It makes data easier to understand and also has a lot more advantages like this. But it also has some disadvantages so for that reason, we are giving here some advantages and disadvantages of graphical representation of data.

Everyone should know the advantages and disadvantages of the graphical representation of data because some people are not aware of the disadvantages of the graphical representation of data. This article will clear the concept of those people.

## Advantages of Graphical Representation of Data

Graphical representation of reports enjoys various advantages which are as follows:

1. Acceptability : Such a report is acceptable to busy persons because it easily highlights the theme of the report. This helps to avoid waste of time.

2. Comparative Analysis : Information can be compared in terms of graphical representation. Such comparative analysis helps for quick understanding and attention.

3. Less cost : Information if descriptive involves huge time to present properly. It involves more money to print the information but the graphical presentation can be made in a short but catchy view to make the report understandable. It obviously involves less cost.

4. Decision Making : Business executives can view the graphs at a glance and can make a decision very quickly which is hardly possible through descriptive reports.

5. Logical Ideas : If tables, designs, and graphs are used to represent information then a logical sequence is created to clear the idea of the audience.

6. Helpful for less literate Audience : Less literate or illiterate people can understand graphical representation easily because it does not involve going through line-by-line and descriptive reports.

7. Less Effort and Time : To present any table, design, image, or graph require less effort and time. Furthermore, such a presentation makes a quick understanding of the information.

8. Less Error and Mistakes : Qualitative or informative or descriptive reports involve errors or mistakes. As graphical representations are exhibited through numerical figures, tables, or graphs, it usually involves fewer errors and mistakes.

9. A complete Idea : Such representation creates a clear and complete idea in the mind of the audience. Reading a hundred pages may not give any scope to make a decision. But an instant view or looking at a glance obviously makes an impression in the mind of the audience regarding the topic or subject.

10. Use in the Notice Board : Such representation can be hung on the notice board to quickly raise the attention of employees in any organization.

## Disadvantages of Graphical Representation of Data

The graphical representation of reports is not free from limitations. The following are the problems with a graphical representation of data or reports:

1. Costly : Graphical representation of reports is costly because it involves images, colors, and paints. A combination of material with human efforts makes the graphical presentation expensive.

2. More time : Normal report involves less time to represent but graphical representation involves more time as it requires graphs and figures which are dependent on more time.

3. Errors and Mistakes : Since graphical representations are complex, there is- each and every chance of errors and mistakes. This causes problems for a better understanding of general people.

4. Lack of Secrecy : Graphical representation makes the full presentation of information that may hamper the objective to keep something secret.

5. Problems to select a suitable method : Information can be presented through various graphical methods and ways. Which should be the suitable method is very hard to select.

6. The problem of Understanding : All may not be able to get the meaning of graphical representation because it involves various technical matters which are complex to general people.

Last, of all, it can be said that graphical representation does not provide proper information to general people.

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## 6 thoughts on “Advantages and Disadvantages of Graphical Representation of Data”

the answers are very good in maybe answering a question on the advantages and disadvantage of using graphical representation of reporting a research findings, as compared to using simple reporting numbers

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## The Effective Use of Graphs

David j. slutsky.

1 The Hand and Wrist Institute, Torrance, California; Assistant Professor, Department of Orthopedics, Harbor-UCLA Medical Center, Los Angeles, California

Graphs are a common method to visually illustrate relationships in the data. The purpose of a graph is to present data that are too numerous or complicated to be described adequately in the text and in less space. Do not, however, use graphs for small amounts of data that could be conveyed succinctly in a sentence. Likewise, do not reiterate the data in the text since it defeats the purpose of using a graph. If the data shows pronounced trends or reveals relations between variables, a graph should be used. If the data doesn't show any significant trend in the evidence, a graph is not the figure of choice. 1

Although there are myriad computer programs that can generate a graph, the author must still heed some basic principles. A basic requirement for a graph is that it is clear and readable. This is determined not only by the font size and symbols but by the type of graph itself. It is important to provide a clear and descriptive legend for each graph. Graphs may have several parts, depending on their format: (1) a figure number, (2) a caption (not a title), (3) a headnote, (4) a data field, (5) axes and scales, (6) symbols, (7) legends, and (8) a credit or source line. For most purposes, design a graph so that the vertical axis (ordinate, Y axis) represents the dependent variable and the horizontal axis (abscissa, X axis) represents the independent variable. Hence, time is always on the X axis. 2 Graphs should always have at minimum a caption, axes and scales, symbols, and a data field. Plotting symbols need to be distinct, legible, and provide good contrast between the figure in the foreground and the background. Open and closed circles provide the best contrast and are more effective than the combination of open circles and open squares. 3 Like the title of the paper itself, each legend should concisely convey as much information as possible about what the graph tells the reader, but it should not provide a summary or interpretation of the results or experimental details. Avoid simply restating the axis labels, such as “temperature vs. time.” It is crucial to choose the correct graph type based on the kind of data to be presented. If the independent and dependent variables are numeric, use line diagrams or scattergrams; if only the dependent variable is numeric, use bar graphs; for proportions, use bar graphs or pie charts. These are briefly described below.

A scattergram is used to show the relationship between two variables and whether their values change in a consistent way, such as analyzing the relationship between the concentration levels of two different proteins.

A line graph is similar to the scattergram except that the X values represent a continuous variable, such as time, temperature, or pressure. It plots a series of related values that depict a change in Y as a function of X. Line graphs usually are designed with the dependent variable on the Y-axis and the independent variable on the horizontal X-axis, such as a Kaplan-Meier analyses survival plots of time-to-event outcomes. The proportion of individuals is represented on the Y-axis as a proportion or percentage, remaining free of or experiencing a specific outcome over time.

A bar graph may consist of either horizontal or vertical columns. The greater the length of the bars, the greater the value. They are used to compare a single variable value between several groups, such as the mean protein concentration levels of a cohort of patients and a control group.

The histogram , also called a frequency distributions graph, is a specialized type of bar graph that resembles a column graph, but without any gaps between the columns. It is used to represent data from the measurement of a continuous variable. Individual data points are grouped together in classes to show the frequency of data in each class. The frequency is measured by the area of the column. These can be used to show how a measured category is distributed along a measured variable. These graphs are typically used, for example, to check if a variable follows a normal distribution, such as the distribution of protein levels between different individuals of a population.

A pie chart shows classes or groups of data in proportion to the whole data set. The entire pie represents all the data, while each slice or segment represents a different class or group within the whole. Each slice should show significant variations. The number of categories should be generally limited to between 3 and 10.

A box plot may be either horizontal or vertical. It is used to display a statistical summary of one or more box-and- variables, such as the minimum, lower quartile, median, and maximum. It may also identify the outlier data. The spacing between the different parts of the box indicates the degree of dispersion and whether the data distribution is symmetrical or skewed.

Some common errors include the following: information in the text is duplicated in graphs, or information in graphs is duplicated in tables. The graph does not have proper legends. The wrong type of graph is chosen to represent the data. The graph is not plotted to scale. Data is not labeled, is inconsistent, interrupted, or exaggerated to produce the desired effect. Another common error is to include a line that suggests an unsubstantiated extrapolation between or beyond the data points. Connecting discrete data points with a continuous line, such as a series of average measurements taken from a group of patients, suggests that there are values between the age groups that fall on the lines, when, in fact, the author cannot know this. A better way to display separate values would be a bar chart, in which each column reflects the average value obtained from each age group. 4 If an extremely large range must be covered and cannot be practically shown with a continuous scale, indicate a discontinuity in the scale and the data field with paired diagonal lines (—//—) indicating a missing extent of the range. 2

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## Graphical Representation of Data

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In today’s world of the internet and connectivity, there is a lot of data available and some or the other method is needed for looking at large data, the patterns, and trends in it. There is an entire branch in mathematics dedicated to dealing with collecting, analyzing, interpreting, and presenting the numerical data in visual form in such a way that it becomes easy to understand and the data becomes easy to compare as well, the branch is known as Statistics . The branch is widely spread and has a plethora of real-life applications such as Business Analytics, demography, astrostatistics, and so on. There are two ways of representing data,

- Pictorial Representation through graphs.

They say, “A picture is worth the thousand words”. It’s always better to represent data in graphical format. Even in Practical Evidence and Surveys, scientists have found that the restoration and understanding of any information is better when it is available in form of visuals as Human beings process data better in visual form than any other form. Does it increase the ability 2 times or 3 times? The answer is it increases the Power of understanding 60,000 times for a normal Human being, the fact is amusing and true at the same time. Let’s look at some of them in detail.

## Types of Graphical Representations

Comparison between different items is best shown with graphs, it becomes easier to compare the crux out of the data pertaining to different items. Let’s look at all the different types of graphical representations briefly:

## Line Graphs

A line graph is used to show how the value of particular variable changes with time. We plot this graph by connecting the points at different values of the variable. It can be useful for analyzing the trends in the data predicting further trends.

A bar graph is a type of graphical representation of the data in which bars of uniform width are drawn with equal spacing between them on one axis (x-axis usually), depicting the variable. The values of the variables are represented by the height of the bars.

## Histograms

This is similar to bar graphs, but it is based frequency of numerical values rather than their actual values. The data is organized into intervals and the bars represent the frequency of the values in that range. That is, it counts how many values of the data lie in a particular range.

## Line Plot

It is a plot that displays data as points and checkmarks above a number line, showing the frequency of the point.

## Stem and Leaf Plot

This is a type of plot in which each value is split into a “leaf”(in most cases, it is the last digit) and “stem”(the other remaining digits). For example: the number 42 is split into leaf (2) and stem (4).

## Box and Whisker Plot

These plots divide the data into four parts to show their summary. They are more concerned about the spread, average, and median of the data.

It is a type of graph which represents the data in form of a circular graph. The circle is divided such that each portion represents a proportion of the whole.

## Graphical Representations used in Maths

Graphs in maths are used to study the relationships between two or more variables that are changing. Statistical data can be summarized in a better way using graphs. There are basically two lines of thoughts of making graphs in maths:

- Value-Based or Time Series Graphs

## Frequency Based

Value-based or time series graphs .

These graphs allow us to study the change of a variable with respect to another variable within a given interval of time. The variables can be anything. Time Series graphs study the change of variable with time. They study the trends, periodic behavior, and patterns in the series. We are more concerned with the values of the variables here rather than the frequency of those values.

Example: Line Graph

These kinds of graphs are more concerned with the distribution of data. How many values lie between a particular range of the variables, and which range has the maximum frequency of the values. They are used to judge a spread and average and sometimes median of a variable under study.

Example: Frequency Polygon, Histograms.

## Principles of Graphical Representations

All types of graphical representations require some rule/principles which are to be followed. These are some algebraic principles. When we plot a graph, there is an origin, and we have our two axes. These two axes divide the plane into four parts called quadrants. The horizontal one is usually called the x-axis and the other one is called the y-axis. The origin is the point where these two axes intersect. The thing we need to keep in mind about the values of the variable on the x-axis is that positive values need to be on the right side of the origin and negative values should be on the left side of the origin. Similarly, for the variable on the y-axis, we need to make sure that the positive values of this variable should be above the x-axis and negative values of this variable must be below the y-axis.

## Advantages and Disadvantages of using Graphical System

Advantages:

- It gives us a summary of the data which is easier to look at and analyze.
- It saves time.
- We can compare and study more than one variable at a time.

Disadvantage:

It usually takes only one aspect of the data and ignores the other. For example, A bar graph does not represent the mean, median, and other statistics of the data.

## General Rules for Graphical Representation of Data

We should keep in mind some things while plotting and designing these graphs. The goal should be a better and clear picture of the data. Following things should be kept in mind while plotting the above graphs:

- Whenever possible, the data source must be mentioned for the viewer.
- Always choose the proper colors and font sizes. They should be chosen to keep in mind that the graphs should look neat.
- The measurement Unit should be mentioned in the top right corner of the graph.
- The proper scale should be chosen while making the graph, it should be chosen such that the graph looks accurate.
- Last but not the least, a suitable title should be chosen.

## Frequency Polygon

A frequency polygon is a graph that is constructed by joining the midpoint of the intervals. The height of the interval or the bin represents the frequency of the values that lie in that interval.

## Sample Problems

Question 1: What are different types of frequency-based plots?

Answer:

Types of frequency based plots: Histogram Frequency Polygon Box Plots

Question 2: A company with an advertising budget of Rs 10,00,00,000 has planned the following expenditure in the different advertising channels such as TV Advertisement, Radio, Facebook, Instagram, and Printed media. The table represents the money spent on different channels.

Draw a bar graph for the following data.

Solution:

Steps: Put each of the channels on the x-axis The height of the bars is decided by the value of each channel.

Question 3: Draw a line plot for the following data

Steps: Put each of the x-axis row value on the x-axis joint the value corresponding to the each value of the x-axis.

Question 4: Make a frequency plot of the following data:

Steps: Draw the class intervals on the x-axis and frequencies on the y-axis. Calculate the mid point of each class interval. Class Interval Mid Point Frequency 0-3 1.5 3 3-6 4.5 4 6-9 7.5 2 9-12 10.5 6 Now join the mid points of the intervals and their corresponding frequencies on the graph. This graph shows both the histogram and frequency polygon for the given distribution.

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## Data Visualization: Definition, Benefits, and Examples

Data visualization helps data professionals tell a story with data. Here’s a definitive guide to data visualization.

Data visualization is a powerful way for people, especially data professionals, to display data so that it can be interpreted easily. It helps tell a story with data, by turning spreadsheets of numbers into stunning graphs and charts.

In this article, you’ll learn all about data visualization, including its definition, benefits, examples, types, and tools. If you decide you want to learn the skills to incorporate it into your job, we'll point you toward online courses you can do from anywhere.

## What is data visualization?

Data visualization is the representation of information and data using charts, graphs, maps, and other visual tools. These visualizations allow us to easily understand any patterns, trends, or outliers in a data set.

Data visualization also presents data to the general public or specific audiences without technical knowledge in an accessible manner. For example, the health agency in a government might provide a map of vaccinated regions.

The purpose of data visualization is to help drive informed decision-making and to add colorful meaning to an otherwise bland database.

## Benefits of data visualization

Data visualization can be used in many contexts in nearly every field, like public policy, finance, marketing, retail, education, sports, history, and more. Here are the benefits of data visualization:

Storytelling: People are drawn to colors and patterns in clothing, arts and culture, architecture, and more. Data is no different—colors and patterns allow us to visualize the story within the data.

Accessibility: Information is shared in an accessible, easy-to-understand manner for a variety of audiences.

Visualize relationships: It’s easier to spot the relationships and patterns within a data set when the information is presented in a graph or chart.

Exploration: More accessible data means more opportunities to explore, collaborate, and inform actionable decisions.

## Data visualization and big data

Companies collect “ big data ” and synthesize it into information. Data visualization helps portray significant insights—like a heat map to illustrate regions where individuals search for mental health assistance. To synthesize all that data, visualization software can be used in conjunction with data collecting software.

## Tools for visualizing data

There are plenty of data visualization tools out there to suit your needs. Before committing to one, consider researching whether you need an open-source site or could simply create a graph using Excel or Google Charts. The following are common data visualization tools that could suit your needs.

Google Charts

ChartBlocks

FusionCharts

## Get started with a free tool

No matter the field, using visual representations to illustrate data can be immensely powerful. Tableau has a free public tool that anyone can use to create stunning visualizations for a school project, non-profit, or small business.

## Types of data visualization

Visualizing data can be as simple as a bar graph or scatter plot but becomes powerful when analyzing, for example, the median age of the United States Congress vis-a-vis the median age of Americans . Here are some common types of data visualizations:

Table: A table is data displayed in rows and columns, which can be easily created in a Word document or Excel spreadsheet.

Chart or graph: Information is presented in tabular form with data displayed along an x and y axis, usually with bars, points, or lines, to represent data in comparison. An infographic is a special type of chart that combines visuals and words to illustrate the data.

Gantt chart: A Gantt chart is a bar chart that portrays a timeline and tasks specifically used in project management.

Pie chart: A pie chart divides data into percentages featured in “slices” of a pie, all adding up to 100%.

Geospatial visualization: Data is depicted in map form with shapes and colors that illustrate the relationship between specific locations, such as a choropleth or heat map.

Dashboard: Data and visualizations are displayed, usually for business purposes, to help analysts understand and present data.

## Data visualization examples

Using data visualization tools, different types of charts and graphs can be created to illustrate important data. These are a few examples of data visualization in the real world:

Data science: Data scientists and researchers have access to libraries using programming languages or tools such as Python or R, which they use to understand and identify patterns in data sets. Tools help these data professionals work more efficiently by coding research with colors, plots, lines, and shapes.

Marketing: Tracking data such as web traffic and social media analytics can help marketers analyze how customers find their products and whether they are early adopters or more of a laggard buyer. Charts and graphs can synthesize data for marketers and stakeholders to better understand these trends.

Finance: Investors and advisors focused on buying and selling stocks, bonds, dividends, and other commodities will analyze the movement of prices over time to determine which are worth purchasing for short- or long-term periods. Line graphs help financial analysts visualize this data, toggling between months, years, and even decades.

Health policy: Policymakers can use choropleth maps, which are divided by geographical area (nations, states, continents) by colors. They can, for example, use these maps to demonstrate the mortality rates of cancer or ebola in different parts of the world.

Tackle big business decisions by backing them up with data analytics. Google's Data Analytics Professional Certificate can boost your skills:

## Jobs that use data visualization

From marketing to data analytics, data visualization is a skill that can be beneficial to many industries. Building your skills in data visualization can help in the following jobs:

Data visualization analyst: As a data visualization analyst (or specialist), you’d be responsible for creating and editing visual content such as maps, charts, and infographics from large data sets.

Data visualization engineer: Data visualization engineers and developers are experts in both maneuvering data with SQL, as well as assisting product teams in creating user-friendly dashboards that enable storytelling.

Data analyst: A data analyst collects, cleans, and interprets data sets to answer questions or solve business problems.

Data is everywhere. In creative roles such as graphic designer , content strategist, or social media specialist, data visualization expertise can help you solve challenging problems. You could create dashboards to track analytics as an email marketer or make infographics as a communications designer.

On the flip side, data professionals can benefit from data visualization skills to tell more impactful stories through data.

Read more: 5 Data Visualization Jobs (+ Ways to Build Your Skills Now)

## Dive into data visualization

Learn the basics of data visualization with the University of California Davis’ Data Visualization with Tableau Specialization . You’ll leverage Tableau’s library of resources to learn best practices for data visualization and storytelling, learning from real-world and journalistic examples. Tableau is one of the most respected and accessible data visualization tools.

To learn more about data visualization using Excel and Cognos Analytics, take a look at IBM’s Data Analysis and Visualization Foundations Specialization .

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## 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.

- 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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## 2: Graphical Representations of Data

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In this chapter, you will study numerical and graphical ways to describe and display your data. This area of statistics is called "Descriptive Statistics." You will learn how to calculate, and even more importantly, how to interpret these measurements and graphs.

- 2.1: Introduction In this chapter, you will study numerical and graphical ways to describe and display your data. This area of statistics is called "Descriptive Statistics." You will learn how to calculate, and even more importantly, how to interpret these measurements and graphs. In this chapter, we will briefly look at stem-and-leaf plots, line graphs, and bar graphs, as well as frequency polygons, and time series graphs. Our emphasis will be on histograms and box plots.
- 2.2: Stem-and-Leaf Graphs (Stemplots), Line Graphs, and Bar Graphs A stem-and-leaf plot is a way to plot data and look at the distribution, where all data values within a class are visible. The advantage in a stem-and-leaf plot is that all values are listed, unlike a histogram, which gives classes of data values. A line graph is often used to represent a set of data values in which a quantity varies with time. These graphs are useful for finding trends. A bar graph is a chart that uses either horizontal or vertical bars to show comparisons among categories.
- 2.3: Histograms, Frequency Polygons, and Time Series Graphs A histogram is a graphic version of a frequency distribution. The graph consists of bars of equal width drawn adjacent to each other. The horizontal scale represents classes of quantitative data values and the vertical scale represents frequencies. The heights of the bars correspond to frequency values. Histograms are typically used for large, continuous, quantitative data sets. A frequency polygon can also be used when graphing large data sets with data points that repeat.
- 2.4: Using Excel to Create Graphs Using technology to create graphs will make the graphs faster to create, more precise, and give the ability to use larger amounts of data. This section focuses on using Excel to create graphs.
- 2.5: Graphs that Deceive It's common to see graphs displayed in a misleading manner in social media and other instances. This could be done purposefully to make a point, or it could be accidental. Either way, it's important to recognize these instances to ensure you are not misled.
- 2.E: Graphical Representations of Data (Exercises) These are homework exercises to accompany the Textmap created for "Introductory Statistics" by OpenStax.

## Contributors and Attributions

Barbara Illowsky and Susan Dean (De Anza College) with many other contributing authors. Content produced by OpenStax College is licensed under a Creative Commons Attribution License 4.0 license. Download for free at http://cnx.org/contents/[email protected] .

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## 8.3.1: Use and Misuse of Graphical Representations

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## Learning Objectives

- Identify which type of graph best represents the data for a given situation.
- Explain how graphs can lead to misinterpretation of data.

## Introduction

In addition to bar graphs , histograms , and circle graphs (pie charts), there are other graphs that statisticians use to represent data and analyze what it shows. But you have to be careful when creating and reading graphs. If they are not carefully created, they can be misleading, and sometimes people purposefully make them misleading.

## Choosing a Graph

Choosing what type of graph to use to represent a specific data set takes some trial and error. And, sometimes, there is more than one appropriate type of graph you can use. What you choose depends on the way you want to present your data, as well as your own personal preferences. Modern spreadsheet programs like Excel are very flexible at creating different types of graphs; with only a couple clicks, you can view data represented as a bar graph, line graph, or circle graph. From there, you can choose which one best paints the picture you want to show.

Since there is often more than one way to graph a data set, let’s look at some examples and think about the different possibilities that are available to us.

A baseball writer wants to create a graph showing the total hits for the players with the greatest number of hits in the first half of the baseball season. These players have the following number of hits: 86, 88, 90, 90, 97, 99, 102, and 106. What type of graph should the writer use to represent the data?

A bar graph or pictograph would work best here. (A pictograph may be preferable for a small amount of data, and a bar graph may be preferable for a lot of data.)

The writer could use a stem-and-leaf plot to show the distribution of numerical data, but this kind of graph is not as effective at showing the relationship between each player and the number of hits that he has. A box-and-whisker plot, which shows the middle of a data set, is not useful here since the writer is interested in the hit totals, not the average number of hits or the spread of the data.

A statistician is collecting data on the frequency with which adults go to the dentist. She surveys 128 people and finds the following information.

- Less than 1 time per year: 28 respondents
- 1 time per year: 51 respondents
- 2 times per year: 42 respondents
- More than 2 times per year: 7 respondents

In a presentation to dentists, she especially wants to highlight the population that visits the dentist less than 1 time per year. What type of graph should she use to represent the data?

A circle graph is best, but a bar graph would also be acceptable.

As with the first example, stem-and-leaf plots and box-and-whisker plots are not useful here. The statistician is not interested in the average amount of times that a person goes to the dentist each year. A line graph would not be appropriate either, as the data is not continuous.

An amusement park planner wants to better understand the distribution of wait times that people experience while waiting for a popular ride. At the park one day, he asks 15 random people about the length of time they had to wait (in minutes).

12, 3, 2, 10, 12, 0, 2, 0, 8, 5, 4, 0, 7, 4, 6

What type of graph provides the best visual representation of this set of data: a circle graph, a box-and-whisker plot, or a bar graph?

An oceanographer wants to make a graph that shows the height (in centimeters) of a specific coral over the period of 2 years. Which type of graph is the most appropriate?

- Circle graph
- Box-and-whisker plot
- Stem-and-leaf plot

Incorrect. A circle graph is often used to show parts of a whole, not changes over time. The correct answer is a line graph.

Incorrect. A box-and-whisker plot is used to show the middle of a data set; it does not reveal much about growth over time. The correct answer is a line graph.

Correct. A line graph mapping height along the y-axis and time along the x-axis is the most appropriate type of graph for this situation.

Incorrect. A stem-and-leaf plot is used to show the spread of a data set; it does not reveal much about growth over time. The correct answer is a line graph.

## Misleading Graphs

As you have seen, graphs provide a visual way to represent data sets. Pictures can be misleading, though, so you also need to know how to identify graphs that seem to show something different than what the data says. This may be due to carelessness or it may be done on purpose. Below are some general questions to keep in mind as you read graphs.

## Questions to Consider when Reading Graphs

- Are the graphs labeled sufficiently?
- What is the scale?
- Does the graph show a full picture of the data, or only a select picture?

Look at the graph that follows. The title states “Average Salary for Adjunct Professors at Four Colleges,” and four bars appear on the graph. You can tell which colleges are being compared, but you are given no information about the scale that is being used. The graph makes it appear that the average salary for Adjunct Professors at Central College is much higher than that at Eastern College, but without a scale, you cannot know for certain. (You do know that the salary is higher; you just do not know how much higher.) To make this graph less misleading, a y-axis with salary information should be included.

Even when both axes are present and labeled correctly, graphical representations of data can be misleading. This is shown in the set of attendance graphs that follow.

In the graph on the left, the scale begins at 0 and goes to 20,000. The graph itself shows that attendance at Minneapolis Wildcats games has steadily increased each year since 2008, topping out in 2010 at just over 16,000.

Now look at the graph on the right. It appears to show that attendance at St. Paul Strikers’ games has increased even more dramatically: the bar for 2010 is more than twice as tall as that in 2008. From looking at these two graphs, you may conclude that the Strikers have been the more popular team recently, as the height of the bars seems to indicate that their attendance has grown faster than that of the Wildcats.

But notice something interesting: the scale of the Strikers graph is very different. It begins at 10,000! This paints a skewed picture of the data when compared with the Wildcats graph, which starts at 0. And by examining the actual data (the actual attendance, not just the height of the bars), you can tell that attendance is actually greater at the Wildcats games. In 2010, for instance, Wildcats attendance is a little over 16,000, while attendance at Strikers games is below 15,000.

This brings up an important point. When you are using graphs to compare data sets, the scales need to be consistent; otherwise, it is very difficult to compare the data itself. As you can tell from the two previous graphs, changing the scale of a graph can dramatically change the way it looks and the impression the graph makes.

A more honest representation of the attendance data can be found in a double-bar graph, where the attendance figures from both teams is mapped side-by-side using the same scale. Look at the results below. Now it is clear that the attendance for the Wildcats is greater than the attendance for the Strikers.

The circle graph here is another example of a misleading representation. The actual percentages of people who responded to each question are not available, and the viewer has to interpret the data based on the size of the sections. At first glance, this graph seems to be showing that a lot of voters seem to be favoring Candidate A, as the “Yes” section is very large.

Part of the reason why this section appears large is because the graph has been created so that it looks large. The circle graph is presented in three-dimensional form, and the data that is foremost in the graph (the “Yes” slice) appears the most prominent. The creator of this graph is hoping that this graph will make you think that Candidate A is very popular!

On closer inspection, though, the data does not seem to support this contention. Combining the “Yes” and “Probably Yes” sections is roughly equal to combining the “No” and “Probably No” sections, which means that the candidate is not as popular as this representation would suggest. In fact, someone who did not want this candidate to appear favorable could have represented the data using the next graph. Notice the different positions of the “No” and “Probably No” sections, as well as the consistent colors.

Notice how perspective and color make a difference in viewing and analyzing data!

Next is a more honest way of representing this data. In this graph, the circle graph is shown from above, and the actual percentages are included.

Results from a poll measuring a politician’s approval rating are shown in the table below.

Which of the following graphs is most misleading?

This line graph accurately displays the data; the axes are labeled appropriately, and the scale is from 0% to 100%. The graph shows that although there has been some up and down variation, the politician’s approval rating has stayed in the mid-50's. The correct answer is Graph B.

This graph uses a very small scale (10%, from 50% to 60%) and has eliminated the final two data points. This graph represents only a part of the data, and is designed to make the reader think that the politician is rated more favorably than he really is.

This bar graph accurately displays the data; the axes are labeled appropriately, and the scale is from 0% to 70%. The politician’s approval ratings look a bit higher in this graph than they do in Graph D, but there is nothing dishonest about this graph. The graph shows that although there has been some up and down variation, the politician’s approval rating has stayed in the mid-50's. The correct answer is Graph B.

This bar graph accurately displays the data; the axes are labeled appropriately, and the scale is from 0% to 100%, with 25% increments. It looks different than Graph C, but the data itself is not misleading: it is the scale of the data that is different. The correct answer is Graph B.

Graphs have a big impact on how you understand a set of data. Use an appropriate type of graph and you can communicate your data effectively; use the wrong type of graph, though, and your viewers may misunderstand the story you are trying to tell. When reading graphs in newspapers and online, be sure to look at the axes, the scale, and the presentation of the data itself. These can all help you identify if the graph is representing a data set fairly or unfairly.

## tableau.com is not available in your region.

## Introduction to Graphs

Table of Contents

15 December 2020

Read time: 6 minutes

## Introduction

What are graphs?

What are the different types of data?

What are the different types of graphical representations?

The graph is nothing but an organized representation of data. It helps us to understand the data. Data are the numerical information collected through observation.

The word data came from the Latin word Datum which means “something given”

After a research question is developed, data is being collected continuously through observation. Then it is organized, summarized, classified, and then represented graphically.

Differences between Data and information: Data is the raw fact without any add on but the information is the meaning derived from data.

## Introduction to Graphs-PDF

The graph is nothing but an organized representation of data. It helps us to understand the data. Data are the numerical information collected through observation. Here is a downloadable PDF to explore more.

- Line and Bar Graphs Application
- Graphs in Mathematics & Statistics

## What are the different Types of Data?

There are two types of Data :

## Quantitative

The data which are statistical or numerical are known as Quantitive data. Quantitive data is generated through. Quantitative data is also known as Structured data. Experiments, Tests, Surveys, Market Report.

Quantitive data is again divided into Continuous data and Discrete data.

## Continuous Data

Continuous data is the data which can have any value. That means Continuous data can give infinite outcomes so it should be grouped before representing on a graph.

- The speed of a vehicle as it passes a checkpoint
- The mass of a cooking apple
- The time taken by a volunteer to perform a task

## Discrete Data

Discrete data can have certain values. That means only a finite number can be categorized as discrete data.

- Numbers of cars sold at a dealership during a given month
- Number of houses in certain block
- Number of fish caught on a fishing trip
- Number of complaints received at the office of airline on a given day
- Number of customers who visit at bank during any given hour
- Number of heads obtained in three tosses of a coin

## Differences between Discrete and Continuous data

- Numerical data could be either discrete or continuous
- Continuous data can take any numerical value (within a range); For example, weight, height, etc.
- There can be an infinite number of possible values in continuous data
- Discrete data can take only certain values by finite ‘jumps’, i.e., it ‘jumps’ from one value to another but does not take any intermediate value between them (For example, number of students in the class)

## Qualitative

Data that deals with description or quality instead of numbers are known as Quantitative data. Qualitative data is also known as unstructured data. Because this type of data is loosely compact and can’t be analyzed conventionally.

## Different Types of Graphical Representations

There are many types of graph we can use to represent data. They are as follows,

A bar graph or chart is a way to represent data by rectangular column or bar. The heights or length of the bar is proportional to the values.

A line graph is a type of graph where the information or data is plotted as some dots which are known as markers and then they are added to each other by a straight line.

The line graph is normally used to represent the data that changes over time.

A histogram graph is a graph where the information is represented along with the height of the rectangular bar. Though it does look like a bar graph, there is a fundamental difference between them. With the histogram, each column represents a range of quantitative data when a bar graph represents categorical variables.

The other name of the pie chart is a circle graph. It is a circular chart where numerical information represents as slices or in fractional form or percentage where the whole circle is 100%.

- Stem and leaf plot

The stem and leaf plot is a way to represents quantitative data according to frequency ranges or frequency distribution.

In the stem and leaf plot, each data is split into stem and leaf, which is 32 will be split into 3 stems and 2 leaves.

Frequency table: Frequency means the number of occurrences of an event. A frequency distribution table is a graph or chart which shows the frequency of events. It is denoted as ‘f’ .

Pictograph or Pictogram is the earliest way to represents data in a pictorial form or by using symbols or images. And each image represents a particular number of things.

According to the above-mentioned Pictograph, the number of Appels sold on Monday is 6x2=12.

- Scatter diagrams

Scatter diagram or scatter plot is a way of graphical representation by using cartesian coordinates of two variables. The plot shows the relationship between two variables. Below there is a data table as well as a Scattergram as per the given data.

## What is the meaning of Graphical representation?

Graphical representation is a way to represent and analyze quantitive data. 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.

## Principles of graphical representation

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 X-axis and y-axis intersected each other at the origin it divides the plane into four parts which are called Quadrant I, Quadrant II, Quadrant III, Quadrant IV.

The location on the coordinate plane is known as the ordered pair and it is written as (x,y). That means the first value will be on the x-axis and the second one is on the y-axis. When we will plot any coordinate, we always have to start counting from the origin and have to move along the x-axis, if it is positive then to the right side, and if it is negative then to the left side. Then from the x-axis, we have to plot the y’s value, which means we have to move up for positive value or down if the value is negative along with the y-axis.

In the following graph, 1st ordered pair (2,3) where both the values of x and y are positive and it is on quadrant I. 2nd ordered pair (-3,1), here the value of x is negative and value of y is positive and it is in quadrant II. 3rd ordered pair (-1.5, -2.5), here the value of x as well as y both are Negative and in quadrant III.

## Methods of representing a frequency distribution

There are four methods to represent a frequency distribution graphically. These are,

- Smoothed Frequency graph
- Cumulative frequency graph or Ogive.
- Pie diagram.

## Advantages and Disadvantages of 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.

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 represents it graphically.

You may also like:

- Graphing a Quadratic Function
- Empirical Relationship Between Mean, Median, and Mode

Not only in mathematics but almost in every field the graph is a very important way to store, analyze, and represents information. After any research work or after any survey the next step is to organize the observation or information and plotting them on a graph paper or plane. The visual representation of information makes the understanding of crucial components or trends easier.

A huge amount of data can be store or analyze in a small space.

The graphical representation of data helps to decide by following the trend.

A complete Idea: Graphical representation constitutes a clear and comprehensive idea in the minds of the audience. Reading a large number (say hundreds) of pages may not help to make a decision. Anyone can get a clear idea just by looking into the graph or design.

Graphs are a very conceptual topic, so it is essential to get a complete understanding of the concept. Graphs are great visual aids and help explain numerous things better, they are important in everyday life. Get better at graphs with us, sign up for a free trial .

## About Cuemath

Cuemath, a student-friendly mathematics and coding platform, conducts regular Online Classes for academics and skill-development, and their Mental Math App, on both iOS and Android , is a one-stop solution for kids to develop multiple skills. Understand the Cuemath Fee structure and sign up for a free trial.

## Frequently Asked Questions (FAQs)

What is data.

Data are characteristics or information, usually numerical, that are collected through observation.

## How do you differentiate between data and information?

Data is the raw fact without any add on but the information is the meaning derived from data.

## What are the types of data?

There are two types of Data:

## 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, organisation and analysis of data as part of the process of a scientific study.

- 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, organisation and analysis of data as part of the process of a scientific study.

## What are the different types of graphs?

Different types of graphs include:

## Guide On Graphical Representation of Data – Types, Importance, Rules, Principles And Advantages

## What are Graphs and Graphical Representation?

Graphs, in the context of data visualization, are visual representations of data using various graphical elements such as charts, graphs, and diagrams. Graphical representation of data , often referred to as graphical presentation or simply graphs which plays a crucial role in conveying information effectively.

## Principles of Graphical Representation

Effective graphical representation follows certain fundamental principles that ensure clarity, accuracy, and usability:Clarity : The primary goal of any graph is to convey information clearly and concisely. Graphs should be designed in a way that allows the audience to quickly grasp the key points without confusion.

- Simplicity: Simplicity is key to effective data visualization. Extraneous details and unnecessary complexity should be avoided to prevent confusion and distraction.
- Relevance: Include only relevant information that contributes to the understanding of the data. Irrelevant or redundant elements can clutter the graph.
- Visualization: Select a graph type that is appropriate for the supplied data. Different graph formats, like bar charts, line graphs, and scatter plots, are appropriate for various sorts of data and relationships.

## Rules for Graphical Representation of Data

Creating effective graphical representations of data requires adherence to certain rules:

- Select the Right Graph: Choosing the appropriate type of graph is essential. For example, bar charts are suitable for comparing categories, while line charts are better for showing trends over time.
- Label Axes Clearly: Axis labels should be descriptive and include units of measurement where applicable. Clear labeling ensures the audience understands the data’s context.
- Use Appropriate Colors: Colors can enhance understanding but should be used judiciously. Avoid overly complex color schemes and ensure that color choices are accessible to all viewers.
- Avoid Misleading Scaling: Scale axes appropriately to prevent exaggeration or distortion of data. Misleading scaling can lead to incorrect interpretations.
- Include Data Sources: Always provide the source of your data. This enhances transparency and credibility.

## Importance of Graphical Representation of Data

Graphical representation of data in statistics is of paramount importance for several reasons:

- Enhances Understanding: Graphs simplify complex data, making it more accessible and understandable to a broad audience, regardless of their statistical expertise.
- Helps Decision-Making: Visual representations of data enable informed decision-making. Decision-makers can easily grasp trends and insights, leading to better choices.
- Engages the Audience: Graphs capture the audience’s attention more effectively than raw data. This engagement is particularly valuable when presenting findings or reports.
- Universal Language: Graphs serve as a universal language that transcends linguistic barriers. They can convey information to a global audience without the need for translation.

## Advantages of Graphical Representation

The advantages of graphical representation of data extend to various aspects of communication and analysis:

- Clarity: Data is presented visually, improving clarity and reducing the likelihood of misinterpretation.
- Efficiency: Graphs enable the quick absorption of information. Key insights can be found in seconds, saving time and effort.
- Memorability: Visuals are more memorable than raw data. Audiences are more likely to retain information presented graphically.
- Problem-Solving: Graphs help in identifying and solving problems by revealing trends, correlations, and outliers that may require further investigation.

## Use of Graphical Representations

Graphical representations find applications in a multitude of fields:

- Business: In the business world, graphs are used to illustrate financial data, track performance metrics, and present market trends. They are invaluable tools for strategic decision-making.
- Science: Scientists employ graphs to visualize experimental results, depict scientific phenomena, and communicate research findings to both colleagues and the general public.
- Education: Educators utilize graphs to teach students about data analysis, statistics, and scientific concepts. Graphs make learning more engaging and memorable.
- Journalism: Journalists rely on graphs to support their stories with data-driven evidence. Graphs make news articles more informative and impactful.

## Types of Graphical Representation

There exists a diverse array of graphical representations, each suited to different data types and purposes. Common types include:

## 1.Bar Charts:

Used to compare categories or discrete data points, often side by side.

## 2. Line Charts:

Ideal for showing trends and changes over time, such as stock market performance or temperature fluctuations.

## 3. Pie Charts:

Display parts of a whole, useful for illustrating proportions or percentages.

## 4. Scatter Plots:

Reveal relationships between two variables and help identify correlations.

## 5. Histograms:

Depict the distribution of data, especially in the context of continuous variables.

In conclusion, the graphical representation of data is an indispensable tool for simplifying complex information, aiding in decision-making, and enhancing communication across diverse fields. By following the principles and rules of effective data visualization, individuals and organizations can harness the power of graphs to convey their messages, support their arguments, and drive informed actions.

## Download PPT of Graphical Representation

## Video On Graphical Representation

## FAQs on Graphical Representation of Data

What is the purpose of graphical representation.

Graphical representation serves the purpose of simplifying complex data, making it more accessible and understandable through visual means.

## Why are graphs and diagrams important?

Graphs and diagrams are crucial because they provide visual clarity, aiding in the comprehension and retention of information.

## How do graphs help learning?

Graphs engage learners by presenting information visually, which enhances understanding and retention, particularly in educational settings.

## Who uses graphs?

Professionals in various fields, including scientists, analysts, educators, and business leaders, use graphs to convey data effectively and support decision-making.

## Where are graphs used in real life?

Graphs are used in real-life scenarios such as business reports, scientific research, news articles, and educational materials to make data more accessible and meaningful.

## Why are graphs important in business?

In business, graphs are vital for analyzing financial data, tracking performance metrics, and making informed decisions, contributing to success.

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## Importance of Graphical Representation of Data

In Statistics, a graphical representation is a visual display of data in the form of a diagram or graph. A chart is a graphical representation of data, in which “the data is represented by symbols, such as bars in a bar chart, lines in a line chart, or slices in a pie chart”. It represents the set of data in a meaningful way. It provides data where it helps to take decisions in a much better way. Some of the various types of graphical representation include – Line Graphs, Bar Graphs, Histograms, etc.

The purpose of a graph is a rapid visualization of a data set. Graphic visual representation of information is a crucial component in understanding and identifying patterns and trends in the ever-increasing flow of data. It encompasses a wide range of techniques to clarify, interpret, and analyze the data by drawing line segments or by plotting the points in the graphs. Graphical representation of reports has various advantages which follow:

- Acceptability:

Such a report is acceptable to busy persons because it easily highlights the theme of the report. This helps to avoid wastage of film.

- Comparative Analysis:

Information can be compared to interns of graphic representation. Graphical representation enables the quick analysis of large amounts of data at one time and can aid in making predictions and informed decisions. Such comparative analysis helps for quick understand and attention.

Information if descriptive involves huge time to present properly. It involves more mono to print the information but the graphical presentation can be made in a short but catchy view to make the report followable. It obviously involves less cost.

- Decision Making:

Business executives can view the graphs at a glance and can make-decision very quickly which is hardly possible through the descriptive report.

- Logical Ideas:

If tables, design, and graphs are used to represent information then a logical sequence is created to clear the idea of the audience.

- Helpful for less literate Audience:

Less literate or illiterate people can understand graphical representation easily because it does not involve going through line by line of any descriptive report.

- Less Effort and Time:

To present any table, design, image, or graphs require less effort and time. Furthermore, such a presentation makes a quick understanding of the information.

- Less Error and Mistakes:

Qualitative or informative or descriptive reports involve errors or mistakes. As graphical representations are exhibited through numerical figures, tables, or graphs, it usually involves less error and mistake.

- A complete Idea:

Such representation creates a clear and complete idea in the mind of the audience. Reading a hundred pages may not give any scope to make a decision. But an instant view or looking at a glance obviously makes an impression in the mind of the audience regarding the topic or subject.

- Use on the Notice Board:

Such representation can be hanged on the notice board to quickly raise the attention of employees in any organization. It also makes 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.

There are different types of graphical representation. Some common of them are as follows:

- Line Graphs – Linear graphs are used to present the continuous data and it is helpful 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 organized into intervals.
- Frequency Table – The table shows the number of pieces of data that falls within the given interval.
- Circle Graph – Also known as a pie chart that shows the relationships of the parts of the whole.

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## The Effect of Textual vs Graphical Representations on Software Design Communication

by Rodi Jolak | May 19, 2020 | article , empirical studies , software engineering | 15 comments

In a study that was published at MoDELS conference in 2018 [1], I reported the results of observing the development efforts of two Software Engineering projects that used Model-based approaches to develop software for two self-driving rovers. I found that most of the effort is spent on collaboration and communication between developers . When it comes to communication, we all know that its quality in Software Engineering projects matters and has a huge impact on the development process and outcome. The quality of commination in Software Engineering is influenced by different factors: human-related (e.g., cognitive, social, and psychological factors), organization-related (flat vs. hierarchical organization), team composition and dynamics, artifacts used in the process (such as communication tools and documentation), etc.

In this post, I want to share some interesting findings from studying the effect of artifacts on development communication . In particular, the impact of software design representation on software design communication. Graphical representations, such as software models, encode and present knowledge differently from textual representations . In particular, they provide a visuospatial representation of information, and can recraft this information into a multitude of forms by using fundamental graphical elements, such as dots and lines, nodes and links [2]. Also, it is known that graphical and textual knowledge representations are differently processed by the human mind .

To understand the effect of graphical vs. textual software design representations on design communication, I coordinated a family of experiments in collaboration with several researchers from four universities in Europe. The experiments are inspired by the Telephone game (or Chinese Whispers ) and involved 120 pairs with a total of 240 participants (See Figure 1). In each randomly formed pair, one participant is randomly appointed as a knowledge explainer and the other one as a knowledge receiver. The participants were asked to give some information about their design knowledge and experience. The knowledge explainers were given a graphical or textual software design describing a MVC design of a software application. The explainers were given 20 minutes to read, understand, and master the design. After that, the explainers had to communicate or explain the design to the receivers in 12 minutes—- using the design artifact. The discussion between the participants was audio-recorded. Finally, the design artifacts (graphical or textual) were taken from the explainers and all the explainers and receivers were invited to answer some questions on the design artifact and the communication experience.

Fig 1. Software Engineering Whispers: Experimental Design

The results of this family of experiments will be soon published by the Empirical Software Engineering journal ( pre-print ).

I have received good news ????We are pleased to inform you that your manuscript, "Software Engineering Whispers: The Effect of Textual Vs. Graphical Software Design Descriptions on Software Design Communication", has been accepted for publication in @emsejournal — Rodi Jolak (@RodiJolak) May 14, 2020

In the following, I summarize the main findings of this family of experiments and discuss some impacts on practitioners.

The first interesting empirical findings is that the users of graphical software design representations had more active discussions about the design than the users of the textual design representations . I mean by active discussions those in which the discussants actively question, inform, and motivate each other. Active discussions increase the quality of communication by contributing to the effectiveness of communication.

Second, we empirically found that graphical design representations support the recall of design details. In other words, the participants who used the graphical artifact during the discussion were able to remember more details about the design than those who used the textual artifact. Certainly, recalling the details of a discussion or the artifacts that were used during that discussion reduces the number of miscommunications, which in turn increases the productivity of the team and ultimately contributes to the satisfaction of stakeholders.

Third, while analyzing the recorded, and further transcribed, discussions between the explainers and receivers, we interestingly observed a difference in the explaining approach between the explainers of the graphical vs. textual artifact . Figure 2 provides an illustration of the observed explaining approaches.

Fig 2. The Explaining Approach of Textual vs. Graphical Software Design Explainers

On the one hand, the explainers of the textual artifact tended to explain the three modules of the MVC design sequentially : Firstly the model entities, then the controllers, and lastly the views, as these modules are orderly presented in the textual document. This trend is intrinsically imposed by the nature of textual representations where the knowledge is presented sequentially on a number of consecutive ordered pages. On the other hand, the explainers of the graphical design had more freedom in explaining the design . Indeed, according to their explaining preferences, the explainers of the graphical artifact tended to jump back and forth between the three MVC modules when explaining the design. Based on this, it seems that a graphical software design representation has an advantage over the textual representation in unleashing explainers’ expressiveness when explaining the design , as well as in helping navigation and getting a better overview of the design.

Based on the previous findings, I provide the following impacts on practitioners:

- Agile development practices include several processes in which communication is at least involved, if not central. Daily meetings are, by definition, the perfect example of agile ceremony which completely relies on communication. Holding daily meetings as a mechanism for design problem solving has positive effects on the communication of the design issues. Accordingly, introducing graphical software design in daily discussions about design decisions would enhance the communication quality between participants , which in turn could strengthen the impact of applying agile practices in software engineering projects.
- Communication is one of the most time-consuming tasks in software development, requiring more effort than any other development activity. The use of graphical software design as a support for design-related communication could be of benefit for productivity . Moreover, minimizing the required effort for communication would provide developers with more time at disposal as well as reduce developers mental load, so they can focus on different tasks.
- By observing the explaining approaches of the explainers of graphical vs. textual software design representants, it seems that the graphical design representing has an advantage over the textual representation in helping navigation and getting a better overview of the design . Even though this requires more investigation, I suggest that, due to its nature, a graphical design provides more adaptability and extra degrees of explaining freedom, which makes it a better pedagogical medium for face-to-face design knowledge transfer.

Now that you have read this post, you can safely state that a picture is indeed worth a thousand words!.

[1] Jolak, R., Ho-Quang, T., Chaudron, M.R.V., & Schiffelers, R.R. “Model-based software engineering: A multiple-case study on challenges and development efforts”. In Proceedings of the 21th ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, pp. 213-223), 2018.

[2] Tversky, B. “Multiple models. In the mind and in the world”. Historical Social Re-search/Historische Sozialforschung . Supplement (31), 59–65, 2018.

## 15 Comments

I could have just looked at the images instead of reading the whole article 😉

Good one!. As long as you access the page I’m fine either way 🙂

Thank you for sharing this – and doing empirical studies as these are really needed.

Another study comparing textual and graphical language also address the scaling: “the larger textual models require increased cognitive eﬀorts on the user side.” It studied a bit different measures (the quality of outcome, its completeness, and the time needed) and found that with graphical languages the quality is better and completing the work also takes less time. Full study available at: https://hal.archives-ouvertes.fr/hal-01345658/document .

Thank you for referring to this interesting study which consolidates the findings that I summarize in this post and provides further benefits of using graphical or diagrammatic representations.

Hi Juha-Pekka, many thanks for the link. I had it, but lost it somehow. /Carsten

Yes, with a textual approach you think in chapters whereas in a model you think in relations. A text has a strictly sequential primary structure with a secondary graph implemented by untyped references. With all graphical modeling approaches I am aware of you have a tree as primary structure and a graph of typed references as secondary structure. Doesn’t look that different at the first glance. But references feel rather unnatural to people thinking in chapters. One the other side for people thinking in relations a reference is the the most natural thing in the world. A relation references an arbitary number of elements. So thinking in relations means thinking in references. And that makes a huge difference. In models the number of relations tends to be number of elements to the power of approx. 1.1. This matches exactly with the COCOMO II prediction. This is no coincidence, but system inherent. With text the number of references is more or less proportianal to the number of words. Given that models tend to describe the system much better than texts. I think in relations, but most of my customers want to get printed paper to be signed manually. That turns out to be no problen at all as it is embarssingly easy to generate text out of models. The other way round is much more tricky. Happy modeling 😉 Carsten

Indeed. Different representations influence the inferences about the relations between elements of the described knowledge. I suggest that you to have a look at the paper of B. Tversky which is referenced in the article (reference number 2).

Hi Rodi, based on my 20+ years experience as facilitator/mentor in system architecture I state the root cause is the way people think. The representation merely follows the way of thinking. So far I am aware of the * text * (cross-)table * model fraction. As mentioned before I am model native and can read text. But the table representation is weird to me. I have to transform the table content into a model to understand. /Carsten

Thank you for our comment Carsten. I agree with you that people think differently. This is a true. But, the causes of this cognitive bias are different e.g., different background, knowledge, experience, expertise, …, used tools, or used knowledge representations or descriptions.

I think we both agree that graphical and textual representations encode knowledge differently and are differently processd by the human mind (please check the following):

Bobek, E., Tversky, B.: Creating visual explanations improves learning. Cognitive Re-search: Principles and Implications1(1), 27 (2016)

Moody, D.L.: The” physics” of notations: a scientific approach to designing visual notations in software engineering. In: 2010 ACM/IEEE 32nd International Conference on Software Engineering, vol. 2, pp. 485–486. IEEE (2010)

As a small addition, one of the IMHO best contributions ever https://en.wikipedia.org/wiki/As_We_May_Think which inspired https://en.wikipedia.org/wiki/Project_Xanadu which was re-invented as https://en.wikipedia.org/wiki/World_Wide_Web

But the root was “As we may think”.

Thank you for sharing this addition 🙂

Thanks Jordi, I’m looking forward to read your preprint. While I agree in the advantages of models vs textual specifications, there’s also questions regarding the understandability of the models as communication artifacts. We recently got a paper accepted at BPM this year with were more than 90 dimensions affecting understandability of business process models were identified. My question is: in your experience, did the visual aids improve the communication for every type of information, or there are cases where a textual representation was more accurate?

> in your experience, did the visual aids improve the communication for every type of information, or there are cases where a textual representation was more accurate?

At this level of generality I really have to state clearly NO.

People being blind from birth tend to have excellent text perception skill, but have difficulties to understand diagrams. Braille displays can “visualize” simple diagrams in a form blind people can recognize.

Even some people able to see get confused by graphical representations. I got aware of it a few years ago setting up a new PC for a school teacher in my neighborhood. The school’s CISO (chief information security officer) told the teachers to use Linux based distributions when working with personal data. So I set up her laptop with plain vanilla Ubuntu and the Gnome3 desktop. I introduced her to the desktop and then she told me: “It is the same problem as with Microsoft Windows I can see the icons — I am not blind — but the icons don’t tell me anything. Is their any way to start application via keyboard commands?”. So I showed her how to do it that way. After a week she came to me and told me: “With the keyboard commands I am at least two times more productive.”.

So it always depends on the people you deal with.

Hi Hugo, the communication aspects that we measured in the paper are four: Explaining, Understanding, Recall, and Inter-Personal Collaborative Communications (that include active discussion, creative conflicts, and conversation management). Considering all the four communication aspects (understanding included) there is a tendency in favor of using the graphical representations, in other words the graphical representation has a positive effect and is the improving condition. Yet, the effect size is statistically significant for active discussion, conversation management, and recall-ability.

Or to answer with another view. Text is strictly sequential or 1-dimensional. Graphics are 2-dimensional (or 3-dimensional https://www.ariscommunity.com/users/pica/2016-11-22-my-ideas-contest-rules-aris-10-years ). Given that graphical representations tend to be more dense. More information is presented on the same area. This can also be contrary productive as some people try to explain how the whole world works within a single diagram. The most “impressive” diagram I have seen so far required two times DIN A0 to be printed at 6pt character size. With text the author has to think about the order to present the topic. A given order can be very helpful to understand. /Carsten

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- Graphs and Graphical Representation

## What are Graphs and Graphical Representation?

Graphical representation refers to the use of charts and graphs to visually analyze and display, interpret numerical value, clarify the qualitative structures. The data is represented by a variety of symbols such as line charts, bars, circles, ratios. Through this, greater insight is stuck in the mind while analyzing the information.

Graphs can easily illustrate the behavior, highlight changes, and can study data points that may sometimes be overlooked. The type of data presentation depends upon the type of data being used.

## Graphical Representation of Data

The graphical representation is simply a way of analyzing numerical data. It comprises a relation between data, information, and ideas in a diagram. Anything portrayed in a graphical manner is easy to understand and is also termed as the most important learning technique. The graphical presentation is always dependent on the type of information conveyed. There are different types of graphical representation. These are as follows:

## Line Graphs:

Also denoted as linear graphs are used to examine continuous data and are also useful in predicting future events in time.

## Histograms:

This graph uses bars to represent the information. The bars represent the frequency of numerical data. All intervals are equal and hence, the width of each bar is also equal.

## Bar Graphs:

These are used to display the categories and compare the data using solid bars. These bars represent the quantities.

## Frequency Table:

This table shows the frequency of data that falls within that given time interval.

## Line Plot:

It shows the frequency of data on a given line number.

## Circle Graph:

It is also known as a pie chart and shows the relationship between the parts of the whole. The circle consists of 100% and other parts shown are in different proportions.

## Scatter Plot:

The diagram shows the relationship between two sets of data. Each dot represents individual information of the data.

## Venn Diagram:

It consists of overlapping circles, each depicting a set. The inner-circle made is a graphical representation.

## Stem and Leaf Plot:

The data is organized from the least value to the highest value. The digits of the least place value form the leaf and that of the highest place value form the stem.

## Box and Whisker Plot:

The data is summarised by dividing it into four parts. Box and whisker show the spread and median of the data.

## Graphical Presentation of Data - Definition

It is a way of analyzing numerical data. It is a sort of chart which shows statistical data in the form of lines or curves which are plotted on the surface. It enables studying the cause and effect relationships between two variables . It helps to measure the extent of change in one variable when another variable changes.

## Principles of Graphical Representation

The variables in the graph are represented using two lines called coordinate axes. The horizontal and vertical axes are denoted by x and y respectively. Their point of intersection is called an origin ‘O’. Considering x-axes, the distance from the origin to the right will take a positive value, and the distance from the origin to the left will take a negative value. Taking the same procedure on y-axes. The points above origin will take the positive values and the points below origin will take negative values. As discussed in the earlier section about the types of graphical representation. There are four most widely used graphs namely histogram, pie diagram, frequency polygon, and ogive frequency graph.

## Rules for Graphical Representation of Data

There are certain rules to effectively represent the information in graphical form. Certain rules are discussed below:

Title: One has to make sure that a suitable title is given to the graph which indicates the presentation subject.

Scale: It should be used efficiently to represent data in an accurate manner.

Measurement unit: It is used to calculate the distance between the box

Index: Differentiate appropriate colors, shades, and design I graph for a better understanding of the information conveyed.

Data sources: Include the source of information at the bottom graph wherever necessary. It adds to the authenticity of the information.

Keep it simple: Construct the graph in an easy to understand manner and keep it simple for the reader to understand. Looking at the graph the information portrayed is easily understandable.

## Importance of Graphical Representation of Data

Some of the importance and advantages of using graphs to interpret data are listed below:

The graph is easiest to understand as the information portrayed is in facts and figures. Any information depicted in facts, figures, comparison grabs our attention, due to which they are memorizable for the long term.

It allows us to relate and compare data for different time periods.

It is used in statistics to determine the mean , mode, and median of different data.

It saves a lot of time as it covers most of the information in facts and figures. This in turn compacts the information.

## FAQs on Graphs and Graphical Representation

Q1. State the Advantages and Disadvantages of Graphical Representation of Data?

Ans: These graphical presentations of data are vital components in analyzing the information. Data visualization is one of the most fundamental approaches to data representation. Its advantages include the following points:

Facilitates and improves learning

Flexibility of use

Understands content

Increase structure thinking

Supports creative thinking

Portrays the whole picture

Improves communication

With advantages, certain disadvantages are also linked to the graphical representation. The disadvantages concern the high cost of human effort, the process of selecting the most appropriate graphical and tabular presentation, creative thinking, greater design to interpret information, visualizing data, and as human resource is used. The potential for human bias plays a huge role.

Q2. What is the Graphical Representation of Data in Statistics?

Graphs are powerful data evaluation tools. They provide a quick visual summary of the information. In statistics information depicted is of mean, mode, and median. Box plots, histograms are used to depict the information. These graphs provide information about ranges, shapes, concentration, extreme values, etc. It studies information between different sets and trends whether increasing or decreasing. Since graphical methods are qualitative, they are not only the basis of comparison and information.

## IMAGES

## VIDEO

## COMMENTS

Graphical representation refers to the use of intuitive charts to visualise clearly and simplify data sets. Data obtained from surveying is ingested into a graphical representation of data software. ... Ans: The advantages of using a graphical method are: 1. Facilitates improved learning 2. Knowing the content 3. Usage of flexibility 4 ...

4. Lack of Secrecy: Graphical representation makes the full presentation of information that may hamper the objective to keep something secret. 5. Problems to select a suitable method: Information can be presented through various graphical methods and ways. Which should be the suitable method is very hard to select.

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 ...

The Effective Use of Graphs. Graphs are a common method to visually illustrate relationships in the data. The purpose of a graph is to present data that are too numerous or complicated to be described adequately in the text and in less space. Do not, however, use graphs for small amounts of data that could be conveyed succinctly in a sentence.

Graph transformation systems manipulate graphs in memory using rules, Graph databases store and query graph-structured data in a transaction-safe, perment manner. Disadvantages of Graph: Limited representation: Graphs can only represent relationships between objects, and not their properties or attributes. This means that in order to fully ...

Advantages and Disadvantages of using Graphical System. Advantages: It gives us a summary of the data which is easier to look at and analyze. ... General Rules for Graphical Representation of Data. We should keep in mind some things while plotting and designing these graphs. The goal should be a better and clear picture of the data.

Some situations, or algorithms that we want to run with graphs as input, call for one representation, and others call for a different representation. Here, we'll see three ways to represent graphs. We'll look at three criteria. One is how much memory, or space, we need in each representation. We'll use asymptotic notation for that.

Data visualization is the representation of information and data using charts, graphs, maps, and other visual tools. These visualizations allow us to easily understand any patterns, trends, or outliers in a data set. Data visualization also presents data to the general public or specific audiences without technical knowledge in an accessible ...

Data visualization is the graphical representation of information and data. By using v isual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data. Additionally, it provides an excellent way for employees or business owners to present data to non ...

Importance of Graphical Representation. Graphical representation gives you a visual presentation of the given data to make it easier to understand. Graphs help you identify different patterns over a short and long period. It assists you in the interpretation of data and comparison of two or more data sets.

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 ...

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 ...

2.1: Introduction. In this chapter, you will study numerical and graphical ways to describe and display your data. This area of statistics is called "Descriptive Statistics." You will learn how to calculate, and even more importantly, how to interpret these measurements and graphs. In this chapter, we will briefly look at stem-and-leaf plots ...

To make this graph less misleading, a y-axis with salary information should be included. Even when both axes are present and labeled correctly, graphical representations of data can be misleading. This is shown in the set of attendance graphs that follow. In the graph on the left, the scale begins at 0 and goes to 20,000.

Better Analysis. Because data visualization is easier to understand and intuitive, it naturally leads to better analysis, because people are more readily able to understand and draw conclusions from vizzes. It's easy to use a visualization to identify patterns, outliers, and trends, which can help when analyzing the data to draw meaningful ...

A graphical representation is the geometrical image of a set of data that preserves its characteristics and displays them at a glance. It is a mathematical picture of data points. It enables us to think about a statistical problem in visual terms. It is an effective tool for the preparation, understanding and interpretation of the collected data.

A graphic representation allows the reader to acquire insights, develop an elaborate understanding, or appreciate new knowledge. Grounded theorists believe that creating visual representations of the emerging theories is an ... The three journals were selected because they use qualitative inquiry and use a broad array of

Advantages and Disadvantages of 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.

The advantages of graphical representation of data extend to various aspects of communication and analysis: Clarity: Data is presented visually, improving clarity and reducing the likelihood of misinterpretation. Efficiency: Graphs enable the quick absorption of information. Key insights can be found in seconds, saving time and effort.

Importance. The purpose of a graph is a rapid visualization of a data set. Graphic visual representation of information is a crucial component in understanding and identifying patterns and trends in the ever-increasing flow of data. It encompasses a wide range of techniques to clarify, interpret, and analyze the data by drawing line segments or ...

Based on this, it seems that a graphical software design representation has an advantage over the textual representation in unleashing explainers' expressiveness when explaining the design, as well as in helping navigation and getting a better overview of the design. Based on the previous findings, I provide the following impacts on ...

vin G. An undirected graph is connected if all vertices are reachable from all other vertices. A directed graph is strongly connected if all vertices are reachable from all other vertices. Cycles. In a directed graph a cycle is a path that starts and ends at the same vertex. A cycle can have length one (i.e. a self loop).

Graphical representation refers to the use of charts and graphs to visually analyze and display, interpret numerical value, clarify the qualitative structures. The data is represented by a variety of symbols such as line charts, bars, circles, ratios. Through this, greater insight is stuck in the mind while analyzing the information.

This International Women's Day (March 8), the Coalition of Partnerships for UHC and Global Health and the Alliance for Gender Equality and UHC are calling for countries to take a gender-responsive approach to achieve universal health coverage (UHC). UHC can only be achieved when gender inequalities and other drivers of inequality within and beyond health systems are addressed.Gender equality ...