# Pie Graph and Bar Graph Contributed by: This pdf includes the following topics:-
Meaning of Graphic Representation of Data
General Principles of Graphic Representation
Methods to Represent a Frequency Distribution
Pie Graph Formula
1. Graphical Representation of Data
Meaning, Principles and Methods
Meaning of Graphic Representation of Data:
Graphic representation is another way of analysing numerical data. A graph is a sort of Graph
through which statistical data are represented in the form of lines or curves drawn across the
coordinated points plotted on its surface.
Graphs enable us in studying the cause and effect relationship between two variables. Graphs
help to measure the extent of change in one variable when another variable changes by a
certain amount.
Graphs also enable us in studying both time series and frequency distribution as they give clear
account and precise picture of problem. Graphs are also easy to understand and eye catching.
General Principles of Graphic Representation:
There are some algebraic principles which apply to all types of graphic representation of data. In
a graph there are two lines called coordinate axes. One is vertical known as Y axis and the
other is horizontal called X axis. These two lines are perpendicular to each other. Where these
two lines intersect each other is called ‘0’ or the Origin. On the X axis the distances right to the
origin have positive value (see fig. 7.1) and distances left to the origin have negative value. On
the Y axis distances above the origin have a positive value and below the origin have a negative
Methods to Represent a Frequency Distribution:
Generally four methods are used to represent a frequency distribution graphically. These are
Histogram, Smoothed frequency graph and Ogive or Cumulative frequency graph and pie
Kinds Graphical Representation:-
1. Pie Graph
2. Bar graph
3. Histogram
4. Frequency Polygon
5. Cumulative Frequency Curve
6. Cumulative Percentage Curve
1. Pie Graph
A pie Graph is a type of graph that represents the data in the circular graph. A pie Graph
requires a list of categorical variables and the numerical variables. Here, the term “pie”
represents the whole and the “slices” represents the parts of the whole. In this article, we will
discuss the definition of a pie Graph, its formula, an example to create a pie Graph, uses,
What is Pie Graph?
2. The “pie Graph” also is known as “circle Graph” divides the circular statistical graphic into
sectors or slices in order to illustrate the numerical problems. Each sector denotes a
proportionate part of the whole. To find out the composition of something, Pie-Graph works the
best at that time. In most of the cases, pie Graphs replace some other graphs like the bar graph,
line plots, histograms etc.
Pie Graph Formula
The pie Graph is an important type of data representation. It contains different segments and
sectors in which each segment and sectors of a pie Graph forms a certain portion of the
total(percentage). The total of all the data is equal to 360°.
The total value of the pie is always 100%.
To work out with the percentage for a pie Graph, follow the steps given below:
Categorize the data
 Calculate the total
 Divide the categories
 Convert into percentages
 Finally, calculate the degrees
Therefore, the pie Graph formula is given as
(Given Data/Total value of Data) × 360°
Example :-
Imagine a teacher surveys her class on the basis of their favourite Sports:
10 5 5 10 10
The data above can be represented by a pie-Graph as following and by using the circle graph
formula i.e. the pie Graph formula given below. It makes the size of portion easy to understand.
Step 1: First, Enter the data into the table.
10 5 5 10 10
Step 2: Add all the values in the table to get the total.
Total students are 40 in this case.
Step 3: Next, divide each value by the total and multiply by 100 to get a per cent:
3. (10/40) × 100 (5/ 40) × 100 (5/40) ×100 (10/ 40) ×100 (10/40)× 100
=25% =12.5% =12.5% =25% =25%
Step 4: Next to know how many degrees for each “pie sector” we need, we will take a full circle
of 360° and follow the calculations below:
The central angle of each component = (Value of each component/sum of values of all the
components) ✕360°
(10/ 40)× 360° (5 / 40) × 360° (5/40) × 360° (10/ 40)× 360° (10/ 40) × 360°
=90° =45° =45° =90° =90°
Now you can draw a pie Graph.
Step 5: Draw a circle and use the protractor to measure the degree of each sector.
Uses of Pie Graph
1. Pie diagram is useful when one wants to picture proportions of the total in a striking way.
2. When a population is stratified and each strata is to be presented as a percentage at that
time pie diagram is used.
 The picture is simple and easy-to-understand
 Data can be represented visually as a fractional part of a whole
 It helps in providing an effective communication tool for the even uninformed audience
4.  Provides a data comparison for the audience at a glance to give an immediate analysis or
to quickly understand information
 No need for readers to examine or measure underlying numbers themselves which can
be removed by using this Graph
 To emphasize a few points you want to make, you can manipulate pieces of data in the
pie Graph
 It becomes less effective, If there are too many pieces of data to use
 If there are too many pieces of data, and even if you add data labels and numbers may
not help here, they themselves may become crowded and hard to read
 As this Graph only represents one data set, You need a series to compare multiple sets
 This may make it more difficult for readers when it comes to analyze and assimilate
2. Bar Diagram
 Also known as a column graph, a bar graph or a bar diagram is a pictorial representation
of data. It is shown in the form of rectangles spaced out with equal spaces between them
and having equal width. The equal width and equal space criteria are important
characteristics of a bar graph.
 Note that the height (or length) of each bar corresponds to the frequency of a particular
observation. You can draw bar graphs both, vertically or horizontally depending on whether
you take the frequency along the vertical or horizontal axes respectively. Let us take an
example to understand how a bar graph is drawn.
Example:-Construct a Bar Graph from following data.
Students No. of Students
Volleyball 25
Football 10
Total = 50
Toabove table depicts the number of students of a class engaged in any one of the three sports
given.Note that the number of students is actually the frequency. So, if we take frequency to be
represented on the y-axis and the sports on the x-axis, taking each unit on the y-axis to be equal to
5 students, we would get a graph that resembles the one below.
The blue rectangles here are called bars. Note that the bars have equal width and are equally
spaced, as mentioned above. This is a simple bar diagram.
5. Histogram is a non-cumulative frequency graph, it is drawn on a natural scale in which the
representative frequencies of the different class of values are represented through vertical
rectangles drawn closed to each other. Measure of central tendency, mode can be easily
determined with the help of this graph.
How to draw a Histogram:
1. Represent the class intervals of the variables along the X axis and their frequencies along the Y-
axis on natural scale.
2. Start X axis with the lower limit of the lowest class interval. When the lower limit happens to be a
distant score from the origin give a break in the X-axis n to indicate that the vertical axis has been
moved in for convenience.
3. Now draw rectangular bars in parallel to Y axis above each of the class intervals with class units
as base: The areas of rectangles must be proportional to the frequencies of the cor¬responding
Example :- Construct a Histogram Graph from following data.
C.I f
20-24 2
25-29 2
30-34 5
35-39 10
40-44 6
45-49 2
50-54 3
6. In this graph we shall take class intervals in the X axis and frequencies in the Y
axis. Before plotting the graph we have to convert the class into their exact limits.
1. It is easy to draw and simple to understand.
2. It helps us to understand the distribution easily and quickly.
3. It is more precise than the polygene.
Limitations of histogram:
1. It is not possible to plot more than one distribution on same axes as histogram.
2. Comparison of more than one frequency distribution on the same axes is not
7. 3. It is not possible to make it smooth.
Uses of histogram:
1. Represents the data in graphic form.
2. Provides the knowledge of how the scores in the group are distributed. Whether
the scores are piled up at the lower or higher end of the distribution or are evenly
and regularly distributed throughout the scale.
3. Frequency Polygon. The frequency polygon is a frequency graph which is drawn
by joining the coordinating points of the mid-values of the class intervals and their
corresponding frequencies.
4.Frequency Polygon
1. Draw a horizontal line at the bottom of graph paper named ‘X’ axis. Mark off the
exact limits of the class intervals along this axis. It is better to start with c.i. of lowest
value. When the lowest score in the distribution is a large number we cannot show it
graphically if we start with the origin. Therefore put a break in the X axis () to
indicate that the vertical axis has been moved in for convenience. Two additional
points may be added to the two extreme ends.
2. Draw a vertical line through the extreme end of the horizontal axis known as OY
axis. Along this line mark off the units to represent the frequencies of the class
intervals. The scale should be chosen in such a way that it will make the largest
frequency (height) of the polygon approximately 75 percent of the width of the
3. Plot the points at a height proportional to the frequencies directly above the point
on the horizontal axis representing the mid-point of each class interval.
4. After plotting all the points on the graph join these points by a series of short
straight lines to form the frequency polygon. In order to complete the figure two
additional intervals at the high end and low end of the distribution should be
included. The frequency of these two intervals will be zero.
Draw a frequency polygon from the following data:
8. In this graph we shall take the class intervals (marks in mathematics) in X axis, and frequencies
(Number of students) in the Y axis. Before plotting the graph we have to convert the c.i. into
their exact limits and extend one c.i. in each end with a frequency of O.
Class intervals with exact limits:
1. It is easy to draw and simple to understand.
2. It is possible to plot two distributions at a time on same axes.
3. Comparison of two distributions can be made through frequency polygon.
4. It is possible to make it smooth.
Limitations of frequency polygon:
1. It is less precise.
2. It is not accurate in terms of area the frequency upon each interval.
Uses of frequency polygon:
1. When two or more distributions are to be compared the frequency polygon is used.
2. It represents the data in graphic form.
3. It provides knowledge of how the scores in one or more group are distributed. Whether the
scores are piled up at the lower or higher end of the distribution or are evenly and regularly
distributed throughout the scale.
9. 5.Cumulative Frequency Curve
Meaning:-This is a diagram that displays cumulative frequency.
To plot this graph first we have to convert, the class intervals into their exact limits. Then we
have to calculate the cumulative frequencies of the distribution.
Now we have to plot the cumulative frequencies in respect to their corresponding class-
Ogive plotted from the data given above:
1. Ogive is useful to determine the number of students below and above a particular score.
2. When the median as a measure of central tendency is wanted.
10. 3. When the quartiles, deciles and percentiles are wanted.
4. By plotting the scores of two groups on a same scale we can compare both the groups.
6.Cumulative percentage Curve
Cumulative percentage is another way of expressing frequency distribution. It calculates the
percentage of the cumulative frequency within each interval, much as relative frequency
distribution calculates the percentage of frequency.
The main advantage of cumulative percentage over cumulative frequency as a measure of
frequency distribution is that it provides an easier way to compare different sets of data.
Cumulative frequency and cumulative percentage graphs are exactly the same, with the
exception of the vertical axis scale. In fact, it is possible to have the two vertical axes, (one for
cumulative frequency and another for cumulative percentage), on the same graph.
Cumulative percentage is calculated by dividing the cumulative frequency by the total number of
observations (n), then multiplying it by 100 (the last value will always be equal to 100%). Thus,
cumulative percentage = (cumulative frequency ÷ n) x 100
Example 1 – Calculating cumulative percentage
For 25 days, the snow depth at Whistler Mountain, B.C. was measured (to the nearest
centimeter) and recorded as follows:
242, 228, 217, 209, 253, 239, 266, 242, 251, 240, 223, 219, 246, 260, 258, 225, 234, 230, 249,
245, 254, 243, 235, 231, 257.
1. The snow depth measurements range from 209 cm to 266 cm. In order to produce the
table, the data are best grouped in class intervals of 10 cm each.
In the Snow depth column, each 10-cm class interval from 200 cm to 270 cm is listed.
The Frequency column records the number of observations that fall within a particular
interval. This column represents the observations in the Tally column, only in numerical
form.
Each of the numbers in the Endpoint column is the highest number in each class interval.
In the interval of 200 cm to 210 cm, the endpoint would be 210.
The Cumulative frequency column lists the total of each frequency added to its
predecessor, as seen in the exercises in the previous section.
The Cumulative percentage column divides the cumulative frequency by the total number
of observations (in this case, 25). The result is then multiplied by 100. This calculation
gives the cumulative percentage for each interval.
11. Snow depth (x) Frequency Endpoint Cumulative Cumulative percentage
(f) frequency
200 0 0 ÷ 25 x 100 = 0
200 to 210 1 210 1 1 ÷ 25 x 100 = 4
210 to 220 2 220 3 3 ÷ 25 x 100 = 12
220 to 230 3 230 6 6 ÷ 25 x 100 = 24
230 to 240 5 240 11 11 ÷ 25 x 100 = 44
240 to 250 7 250 18 18 ÷ 25 x 100 = 72
250 to 260 5 260 23 23 ÷ 25 x 100 = 92
260 to 270 2 270 25 25 ÷ 25 x 100 = 100
2. Apart from the extra axis representing the cumulative percentage, the graph should look
exactly the same as that drawn in Example 2 of the section on Cumulative frequency.
The Cumulative percentage axis is divided into five intervals of 20, while the Cumulative
frequency axis is divided into five intervals of 5. The Snow depth axis is divided by the
endpoints of each 10-cm class interval.
Using each endpoint to plot the graph, you will discover that both the cumulative
frequency and the cumulative percentage land in the same spot. For example, using the
endpoint of 260, plot your point on the 23rd day (cumulative frequency). This point
happens to be in the same place where the cumulative percentage (92%) will be plotted.
You have to be very careful when you are building a graph with two y-axes. For example,
if you have 47 observations, you might be tempted to use intervals of 5 and end your y-
axis at the cumulative frequency of 50. However, when you draw your y-axis for the
cumulative percentage, you must put the 100% interval at the same level as the 47 mark
on the other y-axis—not at the 50 mark. For this example, a cumulative frequency of
47 represents 100% of your data. If you put the 100% at the top of the scale where the
50 interval is marked, your line for the cumulative frequency will not match the line for the
cumulative percentage.
The plotted points join to form an ogive, which often looks similar to a stretched S. Ogives
are used to determine the number, or percentage, of observations that lie above or below
a specified value. For example, according to the table and the graph, 92% of the time the
snow depth recorded in the 25-day period was below the 260 cm mark.
12. The following information can be gained from either the graph or table:
 during the 25-day period, 24% of the time the recorded snow depth was less than
230 cm
 on 7 of the 25 days, snow depth was at least 250 cm