This document summarizes different methods of presenting data, including tabulation, graphical methods, and descriptive statistics. Tabulation involves grouping data into frequency distributions. Graphical methods include bar diagrams, pie charts, histograms, and scatter plots. Descriptive statistics computed from data include rates, ratios, proportions, means, ranges, and standard deviations. These various methods allow large amounts of data to be concisely summarized and visualized.

1. Presentation of Data
Facilitator: Mrs. Jaishree Ganjiwale
Presenter: Dr. Dhruv Patel
1st Year Resident
Community Medicine

2. Data
• A set of values recorded on one or more observational units.
i.e. Object, Person etc.
• Singular - Datum
• Types of Data:
1. Qualitative Data
2. Quantitative Data

3. 1. Qualitative Data:
• Represents a particular quality or attribute.
• There is no notion of magnitude or size of the characteristic, as
they can’t be measured.
• eg. Religion, Sex, Blood group
2. Quantitative Data:
• These data have a magnitude.
• The characteristic is measured either on an interval or a ratio.
• eg. Height in cm, Weight in kg, Haemoglobin (gm%)

4. Variable
• Any character, characteristic or quality that varies is called
variable.
Types
Categorical
Nominal
- Named
categories
eg. Gender,
Marital status
Ordinal
- Category with
implied order
eg. Grading of
BP
Numerical
Discrete
- Whole number
eg. No. of boys,
No of cars
Continuous
- Possibility of
getting fractions
eg. Ht, Wt, Hb
level

5. Methods of Data Presentation
• Data can be presented by following methods:
1) Tabulation
2) Graphical
3) Descriptive statistics

6. Tabulation
• It groups large number of observation and presents the data
very concisely.
• The number of person in each group is called frequency.
• All the frequencies considered together forms the frequency
distribution.
• Tabulation of frequencies may be for:
1) Qualitative Data
2) Quantitative Data

7. 1) Frequency distribution table for Qualitative Data:
• Only frequency varies, characteristic is not variable.
Gender Frequency
Boys 350
Girls 250
Total 600
Fig. Number of boys and girls in medical college

8. 2) Frequency distribution table for Quantitative Data:
• Characteristic and frequency both varies.
• No. of classes should be neither too large nor small.
Approx. No. of classes(k)= 1 + 3.332 log10 N
(where N is number of observation)
• Width of C.I. = Range/ k

9. Height of students
(cm)
Frequency
160-162 10
162-164 15
164-166 17
166-168 19
168-170 20
170-172 26
172-174 29
174-176 30
176-178 22
178-180 12
Total 200
Fig. Quantitative data of height of students in a school

10. Graphical
Qualitative Data
-Bar diagram
-Pie diagram
-Pictogram
-Map diagram
Quantitative Data
-Histogram
-Frequency polygon
-Frequency curve
-Cumulative frequency diagram
-Stem & Leaf plots
-Box and Whisker plot
-Line chart
-Scatter diagram

11. Bar diagram
• Characteristic is plotted on one axis and frequency of data on
another axis.
• Height of the bar indicate the magnitude of the frequency.
• Spacing between any two bars should be nearly equal to half
of the width of the bar.
• Types of bar diagram:
1) Simple bar diagram
2) Multiple bar diagram
3) Component bar diagram

12. 1) Simple bar diagram:
[Raithatha SJ, Shankar SU, Dinesh K. Self-Care Practices among Diabetic Patients in Anand District of Gujarat. ISRN
Family Med. 2014 Feb 11;2014:743791. doi: 10.1155/2014/743791. PMID: 24967330; PMCID: PMC4041263.]
Fig. MPPS (mean performance percentage scores) in the seven domains of self-care
practices among Diabetic Patients in Anand District of Gujarat.
(PA: physical activity; DP: dietary practices; MT: medication taking; MG: monitoring of
glucose; PS: problem solving; FC: foot care; PsA: psychosocial adjustment.)

13. 2) Multiple bar diagram:
• Each observation has more than one value, represented by a
group of bars.
• Two bars are drawn adjacent to each other without spacing &
equal width of the bars is maintained.

14. 6
39
11
44
0
5
10
15
20
25
30
35
40
45
50
Malnourished Normal
Girls
Boys
Fig. Malnourishment status and sex distribution in 100 children
Number

15. 3) Component bar diagram:
• Total height of the bar corresponding to one variable is further
sub-divided into different components.
6
11
39
44
0
10
20
30
40
50
60
Girls Boys
Normal
Malnourished
Fig. Component Bar Chart showing distribution of malnourishment
status in boys and girls
Number

16. Pie diagram
• Consist of a circle, whose area represents total frequency.
• Divided into segments
• Each segment represents a proportional composition of the
total frequency.
• Angle at the center =𝑃𝑒𝑟𝑐𝑒𝑛𝑡𝑎𝑔𝑒 ×
360
100

17. 43%
37%
14%
6%
Distribution of 542 patients according to blood group
A(232)
B(201)
AB(76)
O(33)

18. Pictogram
• Useful for presenting data to common man.

19. Map diagram(Spot map)
Estimated Infant Mortality Rates

20. Histogram
• Similar to the bar chart with the difference that the bars are
adherent.
• Width of the bar represents a class
• Height of the bar represents frequency (If class intervals are
not uniform, then area of the bar represents frequency).

21. Fig. Frequency distribution of serum cholesterol in 86
stroke patients

22. Frequency Polygon
• Derived from a histogram by connecting mid-points of the tops
of the bars in the histogram.

23. Frequency Curve

24. Cumulative Frequency Diagram
• Ogive curve
• Cumulative frequency is obtained by cumulating frequency of
previous classes including the class in question.
• Significance:
– It allows us to quickly estimate the number of observations
that are less than or equal to a particular value.

25. 0
5
10
15
20
25
30
35
40
45
50
55
0 10 20 30 40 50 60 70 80 90 100
Number of students
Cumulative frequency diagram showing marks(%) scored by number of students
Marks(%)
number
of
students

26. Stem and Leaf plots
• To construct stem and leaf plots, divide each value into stem
component & leaf component.
• Digits in tens-place: stem component
Digits in units-place: leaf component
• eg. 15
stem leaf

27. • Sample of 10 people with age
21, 42, 05, 11, 30, 50, 28, 27, 24, 52
8
7
4 2
5 1 1 0 2 0
0 1 2 3 4 5
Stem Leaf
0 5
1 1
2 1 4 7 8
3 0
4 2
5 0 2
Numerical order

28. • Uses:
– To determine whether data symmetrical or skewed
– To check if there is central cluster(mound) or not

29. Box and Whisker plot

30. • Box is located in the center, It spans the interquartile range.
• Median is marked by line inside the box(only graph that shows
median directly).
• Whiskers are the two lines outside the box that extend to the
highest and lowest observed values.
• Also known as Five number summary.
• Displays the center and spread of distribution.

31. Line chart
• It shows relationship between two numeric variables with the
passage of time.

32. Scatter diagram(Dot diagram)
• It is useful to represent correlation between two numeric
variables.
• Correlation can be of two types:
1. Positive correlation
2. Negative correlation

34. Descriptive statistics
1) For Qualitative Data:
– Rate
– Ratio
– Proportion
2) For Quantitative Data:
– Mean
– Range
– Standard Deviation

35. Rate
• Measure of the frequency with which an event occurs in a
defined population over a specified period of time.
• Rate =
𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑒𝑣𝑒𝑛𝑡𝑠 𝑖𝑛 𝑎 𝑝𝑒𝑟𝑖𝑜𝑑 𝑜𝑓 𝑡𝑖𝑚𝑒
𝐷𝑒𝑓𝑖𝑛𝑒𝑑 𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛
× 1000
• Numerator is part of denominator.
• eg. In 1000 population, 30 live births occur in last one year.
– Birth rate =
30
1000
× 1000 = 30 per 1000

36. Ratio
• Shows relative size of two values.
• Numerator is not a part of denominator.
• eg. In 100 population 60 Male & 40 Female
– Ratio of
𝑀𝑎𝑙𝑒
𝐹𝑒𝑚𝑎𝑙𝑒
=
60
40
=
3
2
• Ratio of Male : Female = 3:2

37. Proportion
• To study variation in one or more attributes.
• Indicate relation between individual events & the events in
totality.
• eg. Total 40 students in which 30 girls and 10 boys.
– Proportion of Girls =
𝑁𝑜.𝑜𝑓 𝑔𝑖𝑟𝑙𝑠
𝑇𝑜𝑡𝑎𝑙 𝑠𝑡𝑢𝑑𝑒𝑛𝑡𝑠
=
30
40
= 3:4
– Proportion of Boys =
𝑁𝑜.𝑜𝑓 𝑏𝑜𝑦𝑠
𝑇𝑜𝑡𝑎𝑙 𝑠𝑡𝑢𝑑𝑒𝑛𝑡𝑠
=
10
40
= 1:4

38. Mean
• Sum of all the observations divided by number of observations.
• Mean =
𝑥(𝑠𝑢𝑚 𝑜𝑓 𝑎𝑙𝑙 𝑡ℎ𝑒 𝑜𝑏𝑠𝑒𝑟𝑣𝑎𝑡𝑖𝑜𝑛𝑠)
𝑛(𝑁𝑜.𝑜𝑓 𝑜𝑏𝑠𝑒𝑟𝑣𝑎𝑡𝑖𝑜𝑛𝑠)
• eg. Erythrocyte Sedimentation Rate of 7 subjects are
7, 5, 3, 4, 6, 4, 5
– Mean =
7+5+3+4+6+4+5
7
=
34
7
= 4.8

39. Range
• Difference between the largest value and the smallest value
• Simplest measure of dispersion
• Not a good measure of dispersion as compared with SD, as it
only gives two extreme data values.

40. Standard Deviation
• Most commonly used measure of dispersion
• SD =
(𝑋−𝑋)2
𝑛−1
• Uses:
– Summarises the deviation of a large distribution from mean
– Helps in finding suitable size of sample