2. Principles of presentation of data
Data should be arranged in such a way that it will
arouse interest in reader.
The data should be made sufficiently concise without
losing important details.
The data should presented in simple form to enable
the reader to form quick impressions and to draw
some conclusion, directly or indirectly.
Should facilitate further statistical analysis .
It should define the problem and suggest its solution
3. Methods of presentation of data
The first step in statistical analysis is to
present data in an easy way to be understood.
The two basic ways for data presentation are
Tabulation
Charts and diagram
4. Rules and guidelines for tabular
presentation
Table must be numbered
Brief and self explanatory title must be given to each
table.
The heading of columns and rows must be clear,
sufficient, concise and fully defined.
The data must be presented according to size of
importance, chronologically, alphabetically or
geographically
If data includes rate or proportion, mention the
denominator.
5. Table should not be too large.
Figures needing comparison should be placed
as close as possible.
The classes should be fully defined, should not
lead to any ambiguity.
The classes should be exhaustive i.e. should
include all the given values.
The classes should be mutually exclusive and
non overlapping.
6. The classes should be of equal width or class
interval should be same
Open ended classes should be avoided as far
as possible.
The number of classes should be neither too
large nor too small. Can be 10-20 classes.
Formula for number of classes(K):
K=1+3.322 log10 N, where N is total frequency
7. Tabulation
Can be Simple or Complex depending upon
the number of measurements of single set or
multiple sets of items.
Simple table :
Title: Numbers of cases of Non communicable
diseases in P B M hospital in 2019
Disease Cases
Hypertension 25,000
Diabetes 38,000
Cancer 2,000
Total 65,000
8. Frequency distribution table with
qualitative data
• Title: Cases of Covid-19 in adults and children
in the months of July and August 2020 in P B
M Hospital
Covid-19
cases
July 2020 August 2020 Total
Adult children Adult children
Symptomatic 120 90 320 80 610
Asymptomatic 416 114 612 88 1230
Total 536 204 932 168 1840
9. Frequency distribution table with
quantitative data
• Systolic blood pressure level in Hypertensive
patients at the time of diagnosis
Systolic Blood pressure
(mm of Hg)
No of cases
Male Female Total
<140 14 13 27
140-160 8 6 14
>160 6 4 10
Total 28 23 51
10. Chart and diagram
• Graphic presentations used to illustrate and
clarify information.
• are essential in presentation of scientific data
and diagrams are complementary to
summarize these tables in an easy, attractive
and simple way.
11. • Charts and diagrams are useful methods of
presenting simple data.
• They have powerful impact on imagination of
people.
• Gives information at a glance.
• Diagrams are better retained in memory than
statistical table.
12. • However graphs cannot be substituted for
statistical table, because the graphs cannot
have mathematical treatment where as tables
can be treated mathematically.
• Whenever graphs are compared , the
difference in the scale should be noted.
• It should be remembered that a lot of details
and accuracy of original data is lost in charts
and diagrams, and if we want the real study,
we have to go back to the original data
13. Common diagrams
Pie chart
Simple bar diagram
Multiple bar diagram
Component bar diagram or subdivided bar
diagram
Histogram
Frequency polygon
Frequency curve
14. O give curve
Scatter diagram
Line diagram
Pictogram
Statistical maps
15. Bar diagram
Widely used, easy to prepare tool for
comparing categories of mutually exclusive
discrete data.
Different categories are indicated on one axis
and frequency of data in each category on
another axis.
Length of the bar indicate the magnitude of
the frequency of the character to be
compared.
16. Spacing between the various bar should be
equal to half of the width of the bar.
3 types of bar diagram:
• Simple Multiple
• compound Component
• proportional
18. Multiple diagram
• Each observation has more than one value,
represented by a group of bars.
• Percentage of males and females in different
countries, percentage of deaths from heart
diseases in old and young age, mode of
delivery (cesarean or vaginal) in different
female age groups.
19. Multiple or Compound diagram
0
100
200
300
400
500
600
2015-16 2016-17 2017-18 2018-19 2019-20
Year wise no of patients admitted in ENT hospital
Male
female
20. Component bar chart
• subdivision of a single bar to indicate the
composition of the total divided into sections
according to their relative proportion.
• For example two communities are compared
in their proportion of energy obtained from
various food stuff, each bar represents energy
intake by one community, the height of the
bar is 100, it is divided horizontally into 3
components (Protein, Fat and carbohydrate)
of diet, each component is represented by
different color or shape
21.
22. Histogram
Used for Quantitative, Continuous, Variables.
It is used to present variables which have no
gaps e.g age, weight, height, blood pressure,
blood sugar etc.
It consist of a series of blocks.
The class intervals are given along horizontal
axis and the frequency along the vertical axis.
23.
24. Frequency polygon
Frequency polygon is an area diagram of
frequency distribution over a histogram.
It is a linear representation of a frequency
table and histogram, obtained by joining the
mid points of the hitogram blocks.
Frequency is plotted at the central point of a
group
25.
26. Cumulative frequency diagram or
O’give
Here the frequency of data in each category
represents the sum of data from the category
and the preceding categories.
Cumulative frequencies are plotted opposite
the group limits of the variable.
These points are joined by smooth free hand
curve to get a cumulative frequency diagram
or Ogive.
27.
28. Scatter/ dot diagram
Also called as Correlation diagram ,it is useful to
represent the relationship between two numeric
measurements, each observation being represented
by a point corresponding to its value on each axis.
In negative correlation, the points will be scattered in
downward direction, meaning that the relation
between the two studied measurements is
controversial i.e. if one measure increases the other
decreases While in positive correlation, the points
will be scattered in upward direction.
