Textual / Descriptive Presentation of Data

This is the method of presenting data in text form. This method of presentation is possible only when the quantity of data is not too large.

Tabular Presentation of Data

This is the systematic arrangement of data in rows and columns. This method of presentation helps to simplify and facilitate comparison of data.

Table 4.1 Rural – Urban composition [ 1971-2001 ]
Year Rural Urban
1971 80 20
1981 78 22
1991 74 26
2001 72 28

The table contains 12 entries in 4 rows in and 3 columns. It is called 4 x 3 table giving information on 12 items in 12 boxes. These boxes are called cells of the table. Each cell gives information about an attribute.

Qualitative Classification

Under this method data are classified on the basis of some attribute or quality such as sex, literacy, religion, nationality, occupation etc. They are not measurable.

Table 4.2 Qualitative Classification
Sex Location Total
Rural Urban
Male 57.07 80.80 60.32
Female 30.03 63.30 33.57
Total 44.42 72.71 47.53

Quantitative Classification

Under this method data are classified on the basis of characteristics which are quantitative in nature such as age, height, weight, income, production etc. They are measurable.

Table 4.3 Quantitative Classification
Year Rural Urban
1971 80 20
1981 78 22
1991 74 26
2001 72 28

Temporal Classification

Under this method data are classified on the basis of Time. Time may be in hours, days, weeks, months, years etc.

Table 4.4 Temporal Classification
Year Sale (In lakh)
2007 81.7
2008 86.9
2009 101.4
2010 94.7

Spatial Classification

When classification is done on the basis of place, it is called spatial classification. The place may be village, town, district, state, country, continent etc.

Table 4.5 Spatial Classification
Country Export Share
USA 21.8
UK 5.7

  • Table number is essential for identifying the table.

  • Title gives a description of the contents of the table.

  • Units is a brief statement about units of measurement used.

  • Stubs are the designations of the rows. It is also called row heading.

  • Caption are the designations of the columns. It is also called column heading.

  • Body contains the actual data. It is the most important part of a table.

  • Foot note is a statement meant for giving clarification.

  • Source note indicate the source from where the information is taken.

  • Table should be simple and attractive.
  • It should suit with the size of the paper.
  • The captions and stubs should be arranged systematically.
  • Units of measurement should be specified.
  • Figures may be rounded off as far as possible.
  • It should not be crowded with too much data.

Limitations of Tabulation

  • Tables contain only numerical data, They do not contain details.
  • They can be used only by experts. Common men may not understand them properly.
  • Some tables are very large. They cannot facilitate comparison.

Diagramatic Representation of Data

This is the more attractive and eye-catching method of presenting data. This provides the quickest understanding of the actual situation to be explained by data in comparison to tabular or textual presentations. The types of diagrams are broadly divided into three:

  1. Geometric Diagram.
  2. Frequency Diagram.
  3. Arithmetic Line Graph.

Bar Diagrams

Bar diagrams are the most common type of diagrams used to indicate changes in magnitude over time or space. A bar is a thick or wide line. It is only the length of the bar which is taken into consideration. Breadth of the bar has no significance, but shown only for attention. Therefore, bar diagrams are also called one-dimensional diagrams. A bar diagram may be set.up in horizontal or vertical form. Bars of a bar diagram can be visually compared by their relative height and accordingly data are comprehend quickly. Data for this can be of frequency type or non-frequency type.

The following points must be kept in mind while constructing bar diagrams.

  • The width of the bar should be uniform.
  • The space between the bars should be uniform.
  • The length of the bars should be proportional to the magnitude of the variables that they represent.
  • All the bars should rest on the same line, called the base.


Simple bar diagram is used to represent only one variable. It is easy to draw and simple to understand.


Multiple bar diagram is used for comparing two or more variables. The bars are dawn side by side. In order to distinguish the bars for different variables, it is nice to give different colours or shades to different variables.


In Component bar diagrams, each bar representing the magnitude of a given phenomenon is again sub-divided into various components. Hence it is also called sub-divided bar diagrams. A component bar diagram shows the bar and its sub-divisions into two or more components. Each component occupies a part of the bar proportional to its share in the total. They are very useful in comparing the sizes of different components and also getting an idea about the amount of their contribution.

To construct a component bar diagram, after drawing the base line, a bar is constructed on the line (may be taken as the x-axis) with its height equivalent to the total value of the bar. Then the proportional heights of the components are marked. The rest of the bars are also constructed likewise. Distinguish components by different colours or shades.


Percentage bar diagrams are component bar diagrams drawn on the basis of percentage. Hence the total length of each bar will be 100. This will be useful in comparison of relative characteristics. In this case the bars are all of equal height. Each sub-division shows the percentage to the total.

Pie Diagrams

Pie diagram is also a diagrammatic representation of statistical data. Instead of bars, different segments of a circle represent contribution of various components to the total. In a pie-diagram, a circle is divided into component sectors with areas proportional to the size of the corresponding component. This kind of representation is very much useful because it clearly brings out the relative importance of the various components. For constructing a pie diagram, first we draw a circle of any radius. We convert the component values to percentages and then multiply each percentage value by 3.6°. Then divide the circle into various segments according to these angles. This is because, a circle is having in total 360°, and likewise, it is having 100 equal parts of 3.6°. The following illustrations will make the conversions to percentages and then into angles clear. Let us now consider a data from your class room. Suppose that there are 45 students in your class and you had conducted a survey among them to find their mode of transportation to the school. The data obtained may be presented in a pie-diagram.

  • Let us construct a pie diagram for the following distribution.

    Table 4.6 School Transportation
    Mode of transport No. of students
    School bus 18
    Private vehicle 6
    Public transport 12
    By walking 9

    For constructing the pie diagram, first we have to convert the values to percentages, and then to angles. New table is created as given below.

    Table 4.7 School Transportation
    Mode of transport No. of students Percentage Angle
    School bus 18 \( \mathbf{{{\frac{18 × 100}{45}} }} = 40 \) 40×3.6 = 144º
    Private vehicle 6 \( \mathbf{{{\frac{6 × 100}{45}} }} = 13.33 \) 13.33×3.6 = 48º
    Public transport 12 \( \mathbf{{{\frac{12 × 100}{45}} }} = 26.67 \) 26.67×3.6 = 96º
    By walking 9 \( \mathbf{{{\frac{9 × 100}{45}} }} = 20 \) 20×3.6 = 72º
    Total 45 100 360º

    Now we can draw a circle with any radius. Measure angles 144º, 48º, 96º, and 72º successively. We can beautify diagram using different colours.

    Frequency Diagrams

    Data in the form of grouped frequency distributions are generally represented by frequency diagrams. The commonly used frequency diagrams are:

    1. Histogram.
    2. Frequency Polygon.
    3. Frequency Curve.
    4. Ogive.


    Histogram is a two dimensional diagram of a continuous frequency distribution. In a histogram, rectangles are drawn with class intervals as bases and corresponding frequencies as heights. There are no gaps between the rectangles of a histogram. The scale on the x-axis must be continuous. For that, we convert the frequency table into exclusive, if it was not. If only the midpoints of classes are given, then we determine the upper and lower limits properly. This method is used when we are asked to construct histogram for discrete frequency distribution.

    While constructing the histogram the class limits are taken on the x-axis and the frequencies on the y-axis. If the frequency distribution is having all its classes with equal class intervals, then the rectangles are drawn with uniform width. Then we get rectangles touching each other and each having class interval as their width and frequency as their height. If the class intervals are not uniform, then the widths of the rectangles also vary. This is because, we have to make an adjustment for the unequal classes. For the adjustment, we take that class with lowest class interval. Let it be c. Take any class whose class interval is not c. Divide the class interval by c. Let the answer be k. Then divide the frequency of the class by k. This will give the new frequency of the class. That is,

    k = \( \mathbf{{{\frac{class\,interval\,of\,the\,class}{c}} }} \)

    where c = lowest class interval

    new frequency = \( \mathbf{{{\frac{frequency\,of\,the\,class}{k}} }} \)

    Histogram is used for locating mode of a frequency distribution graphically.

    Table 4.8 Histogram and Bar Diagram
    Bar Diagram Histogram
    Diagramatic presentation of data Diagramatic presentation of data
    Use of rectangles Use of rectangles
    Considers height only Considers both height and width
    Both for discrete and continuous variables Only for continuous variables
    Some space is left between consecutive bars No space is left in between two rectangles
    Can not locate mode Can locate mode

  • Let us draw a histogram for the followuing distribution.
  • Table 4.9
    Marks. Number of Students.
    0 - 10 5
    10 - 20 11
    20 - 30 19
    30 - 40 21
    40 - 50 16
    50 - 60 10
    60 - 70 8
    70 - 80 6
    80 - 90 3
    90 - 100 1

    We will get a beautiful Histogram like this.


    When class intervals are unequal a correction for unequal class-intervals should be made. For making the adjustment, we take that class which has lowest class-interval and adjust the frequencies of other classes. The adjusted frequencies are obtained on dividing the frequency of the given class by the corresponding adjustment factor which is given by

    $$ \mathbf{{{\frac{Magnitude\,of\,the\,class}{Lowest \,class \,interval}} }} $$

  • Let us create a histogram for the following distribution
  • Table 4.10
    Weekly wages ( in ₹ ) Number of Workers.
    10 - 15 7
    15 - 20 19
    20 - 25 27
    25 - 30 15
    30 - 40 12
    40 - 60 12
    60 - 80 8

    Since class-intervals are not uniform, frequencies should be adjusted. The adjustment is as follows:

    The lowest class interval is 5. The frequency of the class 30-40 shall be divided by 2 \( \mathbf{{({\frac{12}{2}} }} = 6) \) since the class interval is double and that of 40-60 by 4 \( \mathbf{{({\frac{12}{4}} }} = 3) \), etc :

    Now we can create a table with equal class intervals and adjusted frequencies.

    Table 4.11
    Weekly wages ( in ₹ ) Number of Workers.
    10 - 15 7
    15 - 20 19
    20 - 25 27
    25 - 30 15
    30 - 35 6
    35 - 40 6
    40 - 45 3
    45 - 50 3
    50 - 55 3
    55 - 60 3
    60 - 65 2
    65 - 70 2
    70 - 75 2
    75 - 80 2

    We will get histogram as given below.


    When only mid-values are given, ascertain the upper and lower limits of the various clsses and then construct the histogram in the same manner.

  • Let us create a histogram for the following distribution.

    Table 4.12
    Mid-value Frequency.
    10 10
    25 24
    35 40
    45 32
    55 20
    65 14
    75 4

    Here only the mid-values are given. Now we need to find the lower and upper limits of each class. These classes would be like the below given table.

    Table 4.13
    Mid-value Frequency.
    10 - 20 10
    20 - 30 24
    30 - 40 40
    40 - 50 32
    50 - 60 20
    60 - 70 14
    70 - 80 4

    Now we can create histogram using the rearranged distribution.

    Locating Mode Graphically


    1. Draw a histogram of the given data.
    2. Draw two lines diagonally in the inside of the modal class bar, starting from each corner of the bar to the upper corner of the adjacent bar.
    3. Then, draw a perpendicular line from the point of intersection to the X - axis, which gives us the modal value.

  • Let us draw a histogram and find mode for the following data
  • Table 4.14
    Profit Number of firms
    0 - 100 12
    100 - 200 18
    200 - 300 27
    300 - 400 20
    400 - 500 17
    500 - 600 6

    Now we can create a diagram showing how mode is located using histogram.

    Comparing Bar Diagram and Histogram

    A histogram may look similar to a bar diagram. But, these two are entirely different, except for the fact that both are diagrammatic representation of statistical data. Their dissimilarity lies in the following facts.

    1. In bar diagram there is gap between bars. In histogram there is no gap between rectangles.
    2. In bar diagram it is the height and not the width or the area of the bar that is insignificant. In histogram both the height and the width are significant.
    3. Bar diagram may be drawn for both discrete and continuous data. But histogram can be drawn only for continuous data.
    4. Bar diagram is not used for locating mode of a distribution. But, histogram is used for locating mode of a distribution graphically.

    Frequency Polygon

    A frequency distribution is a graphical representation of a frequency distribution. It is the most common method of presenting grouped frequency distribution. A polygon is a two dimensional geometric figure formed of three or more straight sides. For constructing a frequency polygon, the class midpoints are marked on the x-axis and frequency on the y-axis. Then frequencies corresponding to each midpoint are plotted and these points are joined by straight lines. The first and the last points plotted are joined to the base line to form a polygon. The rules we had followed for inclusive type frequency distributions and for unequal class intervals while drawing a histogram are very well applicable for frequency polygons also.

    A frequency polygon may be drawn from a histogram also. For that, first we draw the histogram of the given frequency distribution as discussed in the above section. Then midpoints of the upper horizontal sides of the rectangles are marked. The adjacent points are joined by straight lines. Midpoint of the upper vertical side of the first rectangle and that of the last rectangle are joined to the base line to form a polygon.

    A frequency polygon can be fitted to a histogram for studying the shape of the curve. When comparing two or more distributions plotted on the same axes, frequency polygon is likely to be more useful since the vertical and horizontal lines of the distributions may coincide in a histogram.

    Construction of frequency polygon by drawing histogram

  • The contribution given in rupees of 100 shops are distributed as follows:

    Table 4.15
    Contribution Number of shops.
    0 - 500 12
    500 - 1000 18
    1000 - 1500 27
    1500 - 2000 20
    2000 - 2500 17
    2500 - 3000 6

    Let us draw a histogram for the data and show the frequency polygon for it.

    Construction of frequency polygon without drawing histogram

    Let us draw a frequency polygon for the following data which give the contribution given by shops.

    Table 4.16
    Contribution Number of shops.
    0 - 500 12
    500 - 1000 18
    1000 - 1500 27
    1500 - 2000 20
    2000 - 2500 17
    2500 - 3000 6

    Here the distribution is exclusive and class intervals are uniform. For drawing a frequency polygon, first we construct a table with class mid points and corresponding frequencies.

    Table 4.17
    Mid-value Frequency.
    250 12
    750 18
    1250 27
    1750 20
    2250 17
    2750 6

    Now we draw the x and y axes; and mark the class midpoints on the x-axis and fréquency on the y-axis. For each class midpoint, we plot the corresponding frequencies and finally join the points by straight line segments. The Starting point and the endpoints are then joined to the base line. The frequency polygon for the data is given below:

  • Histogram may also be used to represent discrete frequency distribution by taking the given values of the variable as the mid points of continuous classes.
  • We can draw two or three frequency polygons in the same graph paper, but not histogram.
  • Frequency Curve

    A continuous frequency distribution represented by a smooth curve is known as frequency ‘curve. As in the case of drawing frequency polygon, here also, the class midpoints are taken along the x-axis and frequency along the y-axis. The points thus plotted are joined by a smooth curve. It may not necessarily pass through all the points of the frequency polygon, but it passes through them as closely as possible. The curve is drawn in such a way that the area included under the curve is approximately as that of the frequency polygon.

    Frequency curves are used for interpolation. Interpolation is the process of estimating the value of a function that lies between known values, often by means of a graph.

  • Let us draw a histogram, frequency polygon and frequency curve representing the following data.
  • Table 4.18
    Length of leaves Number of leaves
    6.5 - 7.5 5
    7.5 - 8.5 12
    8.5 - 9.5 25
    9.5 - 10.5 48
    10.5 - 11.5 32
    11.5 - 12.5 6
    12.5 - 13.5 1

    Above distribution is shown as frequency curve in the below diagram.

    Ogives or Cumulative Frequency Curve

    Ogives are frequency curves drawn for cumulative frequencies. Therefore ogives are otherwise called cumulative frequency curves.

    We had studied two types of cumulative frequencies; ‘Less than Cumulative Frequency’ and ‘More than Cumulative Frequency’. The frequency curve drawn using less than cumulative frequencies is known as ‘Less than Cumulative Frequency Curve’ or ‘less than Ogive’, and the frequency curve drawn using more than cumulative frequencies is known as ‘More than Cumulative Frequency Curve’ or ‘more than ogive’. For drawing less than ogives, upper class limits are marked on the x-axis and corresponding less than cumulative frequencies on the y-axis; while for drawing more than ogives, lower class limits are marked on the X-axis and corresponding more than cumulative frequencies on the y-axis. Less than ogive is an increasing graph or rising curve and more than ogive is a decreasing graph or declining curve.

    An interesting feature of the ogives is that when we draw them together, their point of intersection will give us the median of the distribution. Median is a measure of central tendency like mode. Ogives can also be used to locate quartile values, deciles or percentiles of a distribution.

  • Let us draw less than and more than ogives using the following data.
  • Table 4.19
    Number of children Number of families
    0 171
    1 82
    2 50
    3 25
    4 13
    5 7
    6 2

    We can arrange the above data in the form of frequency distribution.

    Table 4.20
    Number of children Number of families
    0-1 171
    1-2 82
    2-3 50
    3-4 25
    4-5 13
    5-6 7
    6-7 2

    Now we can arrange data in to less than series.

    Table 4.21
    Number of children Number of families
    Less than 1 171
    " " 2 253
    " " 3 303
    " " 4 328
    " " 5 341
    " " 6 348
    " " 7 350

    Then draw Ogive by less than method.

    We can also arrange data in to more than series.

    Table 4.22
    Number of children Number of families
    0 or more 350
    1 " " 179
    2 " " 97
    3 " " 47
    4 " " 22
    5 " " 9
    6 " " 2

    Then draw Ogive by more than method.

    Locating Median from Ogive

    We have to draw less than and more than ogive to locate median.

    For constructing less than ogive and more than ogive, we want both lower class limits and upper class limits and also both less than cumulative frequencies and more than cumulative frequencies.

    Table 4.23
    Mid-value Less than cumulative frequency More than cumulative frequency
    0 -- 83
    5 2 81
    10 7 76
    15 13 70
    20 21 62
    25 34 49
    30 51 32
    35 62 21
    40 70 13
    45 75 8
    50 79 4
    55 82 1
    60 83 --

    The less than and more than ogives drawn together for the data is given in the below figure.

    Arithmetic Line Graph

    (Time Series Graph)

    Arithmetic line graph is a diagrammatic representation of statistical data over a period of time. Hence it is also called time series. In arithmetic line graphs, values of a variable at different periods of time are shown. The graph shows the changes in the values of a variable with the passage of time. Here, time is the independent variable. The variable which varies according to time, such as, total output of agriculture, the consumption, the national income is known as dependent variable. In time series, time is taken along the x-axis and the dependent variable along, the y-axis. Arithmetic line graphs are used to understand the trend, periodicity, etc., in a long term time series data.

    Consider the data given below of production of steel in a country from 2000 to 2009

    Table 4.24
    Year Production
    2000 9.54
    2001 10.83
    2002 9.12
    2003 8.85
    2004 10.62
    2005 10.85
    2006 10.95
    2007 11.35
    2008 11.65
    2009 11.98

    Here the dependent variable is the production of steel. So it may be marked along the y-axis and time period along the x-axis. Arithmetic line graph for the above data is given in below diagram.

    False Base Line

    If the fluctuations in the values of a variable are vary small as compared to the size of the item, a false baseline is used. By using this even minor fluctuations are magnified so that they are clearly visible on the graph. If the size of items is comparatively big and if the vertical scale begins from zero, the curve would be mostly on the top of the paper and if the differences in the values of various items are not much, it would more or less, be of the shape of a straight line. In false base line the scale from zero to the smallest value of the variable is omitted. Whenever false baseline is Used it should be very clearly indicated on the graph. Generally in such cases, vertical scale is broken in two parts and some blank space is left between them. To make the breaking of vertical scale prominent usually saw-tooth lines are used.

    Advantages of Diagrammatic Presentation of Data

    • Diagrams are easy to understand.
    • Diagrams are more attractive.
    • Diagrams are more appealing.
    • Diagrams save time and labour of the user.
    • It facilitates comparison more quickly