Data type source presentation im

1. Dr. MuhammedirfanH. Momin
Assistant Professor
Community Medicine Department
Government Medical College, Surat

2. Yesterday one patient asked surgeon
Will I survive this risky operation?
surgeon replied:
Yes, I'm absolutely sure that you will survive
the operation.
He asked surgeon :
How can you be so sure?
surgeon replied:
Well, 9 out of 10 patients die in this operation, and
yesterday my ninth patient died.

3. If I had only one more day to live in this
world…
I would spend it in a statistics class
It would seem much longer!
ABOUT MEDICAL STATISTICS

4. Knowledge -Science –Research
Describe the world
Understand the world
Change the world for betterment
Predict events in the world

5. Science
Aspires for certainty
Hierarchy of sciences
Mathematics (Numbers)
Social sciences (society)
Medicine (Human body)
Health sciences (Health of Community)

Objectivity, universality

Certainty
Physics (Natural world)
Constants
Variables
Context

6. •Data collected may be for profileor
prospective studiesat local,
state, national or international level.
•They are analyzedto assess
changes in health or disease
situations in the community or
population by standard parameters.

7. Data
Datum: Measured or counted facts or piece of
information provided in a figure.
Facts, observation & information that comes
from investigation.
Data: A set of Values.
Once the measurements carried out and
recorded values of results is called data.

8. Statistics:
Collection, compilation, presentation, analysis &
interpretation of data.
Uses: Business, demography, economics,
operational research.
Biostatistics:
Medical statistics:Disease, disability or efficacy
of vaccine or new drug.
Health statistics:Public health importance.
Vital statistics:Birth, death, marriage etc.

9. Variable
Any character
Characteristic
Quality
Attribute
A measurable or observable characteristics of a
person or things that take on different values.
E.g. weight
Sex, gender.

10. Parameter
Summery value of a statistic
e.g. mean, median, mode.

11. What is observation
An event and its measurement.
Height: 155 cm
Sex: Male

12. Frequency
Number of persons in each group
is called Frequency…

13. Sources of data
Experiments
Surveys
Records
Experiments & surveys are specially
applied to generate data needed for
specific purpose.
Records provide readymade data

14. Experiments
Carried out in the laboratory.
In the hospital ward.
Fundamental research.
Will provide a data.
Compiled & analyzed

15. Surveys
They provide useful information on
Changing trends in the health status,
morbidity, mortality, nutritional status or
environmental hazards.
Provide feedback which may be expected to
modify policy & system.
Provide timely warning of public health
hazard.

16. Records
Vital statistics
Hospital records

17. Types of data
Qualitative/ quantitative
Discrete/ continuous
Grouped/ ungrouped
Primary/ secondary
Nominal/ ordinal

18. •Primarydata
•isthatwhichiscollectedbythe
researchertoaddressthecurrent
research.
•Secondarydata
•referstodatagatheredbyothersor
fromotherstudieslikeofficialrecords,
publications,documentsetc.

19. Approaches to data collection
Secondary data Primary data
Readily available
Hospital, laboratory ,
blood bank, labour
room
Delivery register, OT
register, case papers
second hand
information
? Purpose served
Descriptive, audit
need to be generated
First hand information
Questionnaire,
interview
schedule, Proforma
Purpose served
Time duration , cost
? Poor existing records

20. •Qualitativedata
•referstodatahavingcountingofthe
individualsforthesamecharacteristicand
notbymeasurement.

21. •There is no notion ofmagnitudeor size
of the characteristicor attributeas the
same cannot be measured
•They are classified by counting the
individuals havingthe same
characteristic or attribute and not by
measurement.
•Persons with the same characteristic
are countedto form specific groups or
classes

22. •Classes such as
Attackedescaped died
cured relieved vaccinated
Males Young Old
treatednot treated on drug etc.
•The characteristicsuch as being attacked by a
disease or being treated by a drug is not a
measurable variable, only the frequency of
persons treated or diseased, varies.
•e.g. By one line of treatment 2o survives out of 25
while on other line of treatment 15 may survive
out of 25

23. •Data are discrete in naturesuch as number of
deaths in different years, population of different
towns, persons with different blood groups in a
population and so on
•In medical studies such data are mostly collected
in
1)Pharmacology to find the action of a drug, in
2)Clinical practice to test or compare the efficacy
of a drug, vaccine, operation or line of treatment
and
3)Demography to find births , deaths still births,
etc.

24. •The resultsthus obtained are
expressed asa ratio, proportion,
rate or percentage.
•The statistical methods
commonly employed in analysis of
such data arestandard error of
proportionand chi-square tests

25. •Quantitativedata
•referstodatahavingmagnitude
andthecharacteristicis
measuredeitheronaninterval
oronaratioscale.
•Theresultingdataaresetof
numbers.

26. Numerical data
These data can be either measuredor
counted.Blood sugar level is a
measured data where as number of
children in a family is a counted data.
Numerical data is also called “interval
data.”
It can be further classified as “discrete
or continuous.”

27. Examples: quantity Data
Height
Age
Size of bicycle frame
Time to complete a statistics test

28. Quantity data can be classified as
‘Discrete or Continuous’
Quantity
data
ContinuousDiscrete

29. Discrete data:
Only certain values are possible
(there are gaps between the possible
values). Implies counting.
Continuous Data
Theoretically, with a fine enough
measuring device.

30. Discrete data--Gaps between possible values-count
0 1 2 3 4 5 6 7
Continuous data--Theoretically,
no gaps between possible values-measure
0 1000

31. Examples:
Discrete Data
Number of children in a family
Number of students passing a stats exam
Number of crimes reported to the police
Number of bicycles sold in a day.
Generally, discrete data are counts.
We would not expect to find 2.2 children in a family or
88.5 students passing an exam or 127.2 crimes being
reported to the police or half a bicycle being sold in
one day.

32. Examples:
Continuous data
Size of bicycle frame
Height
Time to run 500 metres
Age
‘Generally, continuous data come
from measurements.
(any value within an interval is possible with a
fine enough measuring device)

33. Qualitative ( Sex,
Religion)
•Data types
Quantitative
Continuous Discrete
(measurable) (countable)
Age No. of. Children
Hb No. of Cases

34. Quantitative vs. Qualitative
Structured
interviews, Survey
Extensive
Determining
association & cause
Numbers
To arrive at universal
explanations
•Unstructured
interviews; FGD
•Intensive
•Describing and
understanding, explore
•Textual
•To bring out variations
•Empowering,
participative

35. Variables
Category Quantity
Nominal Ordinal
Discrete
(counting)
Continuous
(measuring)
Ordered
categories
Ranks.
Relationships between Variables.
(Source. Rowntree 2000: 33)

36. Categorical data classified as
Nominal, Ordinal, and/or Binary
Categorical data
Not binaryBinary
Ordinal
data
Nominal
data
Binary Not binary

37. Scales of measurement
i)Nominal scale-identify
ii)Ordinal scale-magnitude
iii)Interval Scale-equal interval
Measurement
iv)ratio scale-absolute 0

38. 1.Qualitative/ Categorical variable
Nominal
Ordinal
It can be binary/Dichotomous or
not binary
2.Quantitative/ Numerical variable
Discrete
Continuous

39. CategoricalData/variable
The objects being studied are
grouped into categories
based on some qualitative
trait.
The resulting data are merely
labels or categories.

40. Examples: Categorical Data
Eye color
blue, brown, hazel, green, etc.
Smoking status
smoker, non-smoker
Dead/Alive
Immune/Non immune
O,A,B,AB
Mild, Moderate, Severe

41. Nominal data
A type of categorical data in which
objects fall into unordered categories/
designations.
Similar or dissimilar
Observations are described, based on
certain qualities or properties.
Information fits in to categories but
categories can not be ordered.

42. Nominal data
Sex as a male or female
Dead or alive
Colour of hair
Blood group
All based on certain quality

43. Ordinal data
It is also a categorical data but there is a
natural order among the categories so they
can be ranked or arranged in order.
Data is ordered
Greater than and less than
E.g.
Pain –mild, moderate, severe
PEM
Carcinoma

44. Categorical
Ordinal Nominal
Ordered
categories
Grade of breast
cancer –Better,
same, worsen
Undergraduate &
post graduate
Unordered
categories
Male/ Female
Dead/ Alive
Blood group –O,
A, B, AB

45. Binary /Dichotomous Data
A type of categorical data in which there are
only two categories.
Binary data can either be nominal or ordinal.
Smoking status-smoker, non-smoker
Attendance-present, absent
Class of mark-pass, fail.
Status of student-undergraduate, postgraduate.

46. Conversion of data
Continuous Ordinal Nominal

47. Parametric data
Data whose distribution in the
underlying population can be
represented by normal distribution
(Gussiancurve) are known as
parametric data.
Data of more than 100 number are
considered as parametric data

48. Non parametric data
Underlying population
distribution is not normal.
Distribution free
Unknown distribution

49. Univariate, bivariateor multivariate
data
When we try to analyze only one
variableat a time in a particular study
sample, the data set is called univariate

50. Univariate
Age at diagnosis of
diabetes
Sex of diabetic patients
No Age
1 43
2 47
3 49
4 60
5 55
6 40
7 63
8 41
9 56
10 55
No Sex
1 M
2 M
3 F
4 M
5 F
6 F
7 M
8 F
9 M
10 M

51. Bivariate
No Age sex
1 43 M
2 47 M
3 49 F
4 60 M
5 55 F
6 40 F
7 63 M
8 41 F
9 56 M
10 55 M

52. Multivariate
No Age sex Medicines
used
1 43 M A
2 47 M A
3 49 F B
4 60 M A
5 55 F C
6 40 F D
7 63 M A+B
8 41 F C
9 56 M A
10 55 M B

54. PRESENTATION OF
STATISTICAL DATA

55. •Two main methods
1) Tabulation
2) Drawing

56. 1.Become concisewithout losing its detail.
2. Become simpleto form impression.
3. Arouse interestin the reader.
4.Become helpfulin further statistical
analysis.
Presentation should be such that data

57. TABULATION

58. •They are devicesfor presenting
data from a mass of statistical data.
•Preparation of frequency
distribution tableis the first
requirement.
•Tablecan be simpleor complex
depending upon measurement of
single set of items or multiple sets
of items.

59. •In most studies, information is collected in
large quantityand the data should be
classifiedand presented in the form ofa
frequency distribution table.
•It groups large number of seriesor
observations of master tableand presents
the data very concisely, giving all
information at a glance.

60. •All the frequencies considered together form the
frequency distribution.
•The number of persons in each group is called
the frequency of that group.
•It records how frequentlya characteristicor an
eventoccurs in persons of the same group
•The frequency distribution table of most
biological variablesdevelops a distribution
which can be compared with the standard
distributions such as normal , binomial or
Poisson.
•Tabulation of frequencies may be for Qualitative
data or Quantitative data

61. •Classification:Dividingthetotalgroupofobservations
intosmallergroupsaccordingtosimilaritiesor
dissimilaritiesoftheitemsw.r.t.characterunderstudy.
•Thequantitativetypeofdatacanbeclassifiedby
dividingtheRangeintosuitablenumberofGroupsor
ClassIntervals.
Tabular presentation
0 20 40 60 80
-Age-
C.I. : 0 –20, 20-40, 40 –60 and 60 -80

62. 1. Find Min. & Max.(9.1 & 15.7)
2. Calculate difference
(Max. –Min.) (15.7 –9.1 = 6.6)
3. Decide No. of Classes ( 5-15)
4. Decide width of classes (Equal /Unequal)
GUIDELINES TO PREPARE A TABLE

63. 5. Decide class limits (Closed / Open )
Precise ( 9.0 -9.9 / 9 -10 )
Non-overlapping ( 9.0 -9.9, 10.0 -10.9,
…/
9 -10, 10 -11…)
6. Prepare a dummy table(Hb, Tally,
Frequency)
7. Tabulate (using tally marks)
GUIDELINES TO PREPARE A TABLE

64. Steps in tabular presentation of quantitative data
Range=Largestobservation-Smallestobservation
K=NumberofClassIntervals(C.I.)=1+3.322xlogn
orchooseKaccordingtoyourinterest.
W=ClassInterval=Range/K
Preparetablemainlywiththreecolumnswith
headingsC.I.,frequencyandpercentageandKrows
representingC.I.e.g.110-120,120-130,……,etc.
StartC.I.withsmallestobservation.

65. Type of class intervals
•Closed ended C.I. e.g. 10-15
•Open ended C.I. e.g. <20 or >=30
•BMI: <18, 18-25, 25-30, 30+
Range=49-10= 39 yrs, K=4 and W =10 yrs
Inclusive type Exclusive type
C.I.
10 –19
20 -29
30 -39
40 -49
C.I.
10 -20
20 -30
30 -40
40 -50

66. 1. Number (To refer )
2. Title (What, How classified, Where & When)
3. Column headings (concise & clear)
4. Foot-note (Headings, Special cell, Source)
ELEMENTS OF A TABLE

67. •Rawdataisarrangedintheorderinwhichtheyare
collected.
Age: 39, 47, 51, 44, 39, 35, 30, 45, 50, 41, 45, 46, 55,
49, 53, 37, 36, 32, 34, 59,33, 55, 45, 40, 32, 36, 48, 50,
38, 44, 50,52
Presentation of data
•Datainthisformisdifficulttounderstandand
interpret.
•Togetinformationfromtherawdatatheymustbe
organizedinsomeorderlyfashion.
Age Number
30-39 12
40-49 11
50-59 9

68. Pt. No. Hb. Pt. No. Hb. Pt. No. Hb.
112.0 1111.2 2114.9
211.9 1213.6 2212.2
311.5 1310.8 2312.2
414.2 1412.3 2411.4
512.3 1512.3 2510.7
613.0 1615.7 2612.7
710.5 1712.6 2711.8
812.8 18 9.1 2815.1
913.5 1912.9 2913.4
1011.2 2014.6 3013.1
SAMPLE DATA SET

69. Hb (g/dl) No. of patients
9.0 –9.9 1
10.0 –10.9 3
11.0 –11.9 6
12.0 –12.9 10
13.0 –13.9 5
14.0 –14.9 3
15.0 –15.9 2
Total 30
TABLE FREQUENCY DISTRIBUTION OF
30 ADULT MALE PATIENTS BY Hb

70. In Qualitative data
•There is no notion of
magnitudeor size of attribute,
hence the presentation of
frequency distribution is very
simple because the
characteristic is not variable but
discrete

71. Sex Colour of Cloths Total
WhitePinkGreenYellowBlue
Boys5 6 6 2 2 21
Girls16 1217 10 14 69
Total21 1823 12 16 90

72. Dimension = No. of variables according to which
the data are classified
One-way Table -Freq. distn. of 30 adult male pts. by Hb
Two-way Table -Freq. distn. of 30 adult pts. by Hb& Sex
Three-way Table -Freq. distn. of 30 pts. by Hb, Sex & Age
Four Way Table-Freq. distn. of 30 pts. by Hb, Sex, Age &
Caste
DIMENSION OF A TABLE

73. Blood GroupNumber(%)
A 10(20)
B 12(24)
AB 13(26)
O 15(30)
Total 50(100)
The dist of blood group of patients
with Lung Cancer

74. Socioeconomic Status
Awareness about HIV/AIDS
PoorSatisfactoryV GoodTotal
Low 30 15 05 050
Middle 15 22 13 050
High 10 25 15 050
Total 55 62 33 150
Two way presentation
Awareness about HIV/AIDS by
socioeconomic status.

75. PERSONAL
ITY TYPE
SBP140 SBP<140
CHD
yes
No
CHDTot
CHD
yes
No
CHDTot
A 6931938810910921201
B 27 2783055212081260
Total 9659769316123002461
OR = 2.28 OR=2.32
Three way presentation
Personality type by SBP by CHD

76. PERSONALITY
TYPE
SBP140 SBP<140
CHD No CHDTotCHDNo CHDTot
A 69 319 388 10910921201
B 27 278 305 52 12081260
Total 96 597 693 16123002461
Four way presentation
Personality type by SBP by CHD by Sex
Male:
PERSONALITY
TYPE
SBP140 SBP<140
CHD No CHD Tot CHD No CHD Tot
A 69 319 388 109 1092 1201
B 27 278 305 52 1208 1260
Total 96 597 693 161 2300 2461
Female:

77. DIAGRAMS

78. Graphical presentation of data
Graphical presentation of data is useful for
•Giving a visual impression of the data.
•Studying hidden patterns or relationships
in the data.
•Identifying outliers or extreme
observations.
•Easy and quick understanding.

79. Type of Variable Diagram
Qualitativeor discrete Bar diagram
(religion, gender, Pie/sector chart
place of residence) Pictogram
Map diagram
Spot map
TYPES OF DIAGRAMS

80. Type of Variable Diagram
Continuous
(height, weight, blood sugar )
1.Histograms.
2.Frequency polygon
3.Frequency curve
4.Line Chart / diagrams
5.Cumulative Frequency Diagram
6.Scatter or dot diagram
7.Box and whiskers ( The Box Plot )
8.Stem and leaf display( Stem plot)
TYPES OF DIAGRAMS

81. •Used when data are qualitativeor discrete
•Height of a bar is proportional to the frequency
•Width of each bar is same.
•Multiple bars can be drawn in the same
diagram.
BAR
DIAGRAM

82. Simple Bar Diagram
It is mainly used for the presentation of
qualitative data.
The frequency distribution of blood group
of patients with Lung Cancer
Blood Group Freq
A 25
B 45
AB 15
O 20

83. Group Throat
cancer
Lung Cancer
A 20 25
B 48 45
AB 10 15
O 22 20
The frequency distribution of blood group of
patients with Lung Cancer and Throat cancer
In Multiple bar diagram two or more bars can be
grouped together.
Multiple Bar Diagram

85. DR IRFAN MOMIN
AIDS Awareness
94
73
81
46
57
80
UrbanRuralTotal
Percent of women and men age 15-49 who have
heard of AIDS
Women
Men

86. Component Bar Diagram/
ProportionalBar Diagram
Thebarsmaybedividedintotwo
ormoreparts,eachpart
representsacertainitemand
proportionaltothemagnitudeof
thatparticularitem.

87. Status
Awareness about HIV/AIDS
PoorSatisfactor
y
V GoodTotal
Low 30 15 05 050
Middle 15 22 13 050
High 10 25 15 050
Total 55 62 33 150
Component Bar Diagram
Awareness about HIV/AIDS by
socioeconomic status.

88. Component Bar Diagram
Level of knowledge
SES

89. 0
250
500
750
1000
1250
1500
1750
2000
19981999200020012002200320042005200620072008200920102011* Wild poliovirus cases, India
P1 wild P3 wild
* data as on 2 March 2012
1 WPV case in 2011 compared to 42 in
2010

90. Three dimensional bar diagram
In three dimensional axis, along one of the axis
frequency / proportion/percentage is marked while on
other axis categories of two characteristics are marked.
TheproportionaldistributionofIHDpatientsbysystolic
bloodpressurebyserumcholesterollevels.
SBP
Serum Cholesterol Levels< 140140 -160> 160
<180 0.06 0.08 0.11
180 -200 0.12 0.14 0.16
200 -260 0.30 0.28 0.29
> 260 0.52 0.50 0.44

91. Three dimensional bar diagram

92. Itiscommonlyusedforthepresentationofqualitative
typeofdata.
Circleisusedforthepresentationofdata,area
enclosedbyitbeingtakenas100%.
Circleisdividedintonumberofsectorsbydrawing
anglesatthecentre.
Areaofeachsectorvarieswiththecorresponding
frequencyorpercentage.
Sincefullangleatthecentreis360,forany
particularcategorytheangleshouldbe3.6times
correspondingfrequencyorpercentage.
Pie diagram/chart

93. Table -2 Distribution of newly detected leprosy
patients by Type, Govt. Leprosy Treatment & Study
Centre, Arakandanallur, 1955-57Patients Type
No. %
Angle
(Degrees)
L

N?L

N

689

157

2999
17.9

4.1

78.0

64

15

281

Total 3845 100.0 360

94. N
78%
L
18%
N?L
4% Table -2 Distribution of newly detected leprosy
patients by Type, Govt. Leprosy Treatment & Study
Centre, Arakandanallur, 1955-57

95. GroupFrequencyPercentageAngle
A 10 20 72
B 25 50 180
AB 08 16 57.6
O 07 14 50.4
Freq dist of blood group of patients
(Lung Cancer)

96. Pie Chart

97. Total
Employees
1493 (100%)
Total
Hypertensive
455 (30.5%)
Known
Hypertensive
197 (13.2%)
RULE OF HALVES
Employees on
regular
Treatment 139
(9.3%)
Employees
having
controlled
Hypertension
71 (4.7%)
DR IRFAN MOMIN

98. Sex-ratio (0-6 yrs)
-914
DR IRFAN MOMIN

99. Navsari
Valsad–2.
18
Dang–4.26
Surat-1. 39
Narmada-1.26
Bharuch
Vadodara –
1.10
PM
2.22
Dahod-2.25
P.R.-0.87
2.7
7
2. 04
Endemicityof Leprosy –March.09
> 5
2 -3
1 -2
< 1
P.R.
3 -5
Gujarat achieved elimination as
on Oct-04
DR IRFAN MOMIN

100. Pictogram
•Itiscommonlyusedforthepresentationof
qualitativetypeofdata.
•Heresuitablesymbolisfirstchosento
representcertainnumberofunitsofvariable.
•Nexteachvalueinthegivenseriesofdatais
representedeitherbytakingsimilarsymbol,its
sizebeingproportionaltothevalueorbytaking
numberofsymbolsofsamesize.

101. Freq distribution of bld gr of patients with Lung Cancer.
Group Freq SymbolNo of units of variable
A 25  5
B 30  10
AB 20  10
O 5  1




Pictogram

102. Histogram
Itisusedforthepresentationofquantitativetypeof
data.
Alongoneoftheaxisfrequencyismarkedwhileonthe
otheraxisclassintervalsorscaleismarked.
Frequencyofeachgroupwillformrectangleorcolumn.
Alltherectanglesareadjacenttoeachother.

103. •Essentially a bar diagram
•Bars are drawn continuously
•Width -usually equal
•Area -proportional to frequencies
HISTOGRAM

104. Table 3 Frequency distribution of Haemoglobin
levels of adult male patients (n=30)Hb (g/dl)

No. of patients

9.0 - 9.9
10.0 - 10.9
11.0 - 11.9
12.0 - 12.9
13.0 - 13.9
14.0 - 14.9
15.0 - 15.9
1
3
6
10
5
3
2

Total

30

105. 0
2
4
6
8
10
12
9.0 - 9.9 10.0 - 10.911.0 - 11.912.0 - 12.913.0 - 13.914.0 - 14.915.0 - 15.9
Hb level (g/dl)
No. of patients Fig. 3 Frequency distribution of
Haemoglobin levels
of adult male patients (n=30)

106. Freq dist of Pulse Rate
Pulse Rate Frequency
30 –35 5
35 –40 9
40 –45 13
45 –50 18
50 –55 15
55 –60 11
60 –65 8
65 –70 7
70 –75 5
75 –80 3
Note: C.I. should exclusive type

107. Histogram

108. Frequency Polygon

109. Frequency Curve

110. Stem and leaf diagram
86
90 0 2 2 4 4 6 6 8
100 0 0 2 2 2 4 4 4 6 6 7 8 9
110 0 0 2 2 4 4 4 6 6 6 7 7 7 8 8 8 8 9 9 9 9 9
120 0 0 0 0 0 0 1 1 1 2 2 2 3 4 4 4 5 56 6 6 7 8 8 9 9 9
130 0 1 2 3 4 5 6 7 8 9
140 1 2 3 4 5 6
150 1 2

111. •Diagram is drawn by taking
X –axis -time (e.g., Years)
Y –axis -value of any index or quantity
(e.g., couple protection rate)
•Displayshow a variable has changed over time
LINE DIAGRAM

112. Line chart
Linechartsarecommonlyforusedstudying
chronologicalvariation(trend)inagivensetof
data.
Time
F
r
e
q

113. Table 4 Number of smear-positive new leprosy
cases registered at the Acworth Municipal
Leprosy Hospital, Mumbai, 1985-1995
Year
No. of cases
Registered
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1681
1319
1143
1287
1317
1103
1060
1176
825
706
528

Source:Juwatkar PS, Chulawala RC, Naik SS.Correspondence
Indian J Lepr 1997;62 (2):197

114. 0
500
1000
1500
2000
19851986198719881989199019911992199319941995 Fig 4 Number of smear-positive new leprosy cases
registered at the Acworth Municipal Leprosy
Hospital, Mumbai, 1985-1995

115. Line chart
Table shows year-wise distribution of Morbidity
Rate per 1000 due to all causes in three services.
Year Army Navy AF
88 123 121 143
89 125 120 134
90 119 115 131
91 116 133 123
92 121 125 120
93 119 117 112
94 124 95 114
95 129 116 110
96 143 97 117
97 143 140 100

117. Sex Ratio & Child Sex-Ratio
DR IRFAN MOMIN

118. ScatterDiagram:
Itisusedtodepicttherelationship
betweentwovariableswhichare
quantitative.Oneofthevariableis
measuredalongX-axiswhileother
variableismeasuredalongY-axis.
Seriesofpairobservationssuchas(x,y)
markedinX-Yaxissystem.

119. Diagramshowstherelationshipbetweenheight
andweightforchildrenofage10yrs.
0
10
20
30
40
50
60
70
125 130 135 140 145 150 155 160
The relationship between height and weight
Height
W
e
i
g
h
t

120. Dot Diagram:
It used for presentation of quantitative data. Scale is
marked on a line and then each value is represented by
a dot.
Data on age: 40, 41, 41, 41, 45, 46, 45, 46,
47, 47, 47, 50, 50, 50, 50, 56, 56, 57, 57, 58, 59,
60, 60

121. BOX AND WHISKERS DIAGRAM

122. PARTITION VALUES:
The values which divide the given data in to
number of equal parts are called the partition
values/ percentiles.
The most commonly used partition values are
QUARTILES, QUINTILES, DECILES, PERCENTILE
S.

123. QUARTILES:
The values which divide the given data in to
four equal partswhen observations are
arranged in order of magnitude are
quartiles.
obviously there will be three quartiles
Q1,Q2 & Q3.
Q1(1
st
quartile):25%below &75%above
Q2(2
nd
quartile): same as median 50% above
& below
Q3(3
rd
quartile):75%below &25% above

124. QUINTILES & DECILES:
Quintiles : It contains four
pointsso it will divide data in to
five equal parts.
Deciles : it contain 9 points & it
will divide data in to ten equal
parts.

125. Quartile
For quartiles, we want to divide our data
into 4 equal pieces.
Suppose we had the following data set (already
in order)
2 3 7 8 8 8 9 13 17 20 21 21

126. Box and Whisker plot
•Itisusedforthepresentationofquantitative
typeofdata.
•Itisusefulincomparingthedistoftwoormore
groups.
•Determine:smallest,largest,Q1,Q2andQ3
•MarkthescaleonYaxis(orXaxis).
•Drawabox(arectanglewithwidthasmuchas
possibleandlengthasQ3-Q1)withendstrough
Q3&Q1.

127. Box and Whisker plot
•DrawahorizontallinethroughtheboxatQ2.
•Drawthewhiskers(lines)fromeachendof
theboxtothesmallestandlargestvalues.
•Moreextremeobservationsareplotted
individually.
Obs<Q
1
–1.5*(Q
3
-Q
1
)or>Q
3
+1.5*(Q
3
-Q
1
)and
smallestandlargestvalues:afterexcluding
extremeobservations.

128. Box and Whisker plot
Largest Value
Smallest Value
Q
3
Q
2
Q
1
S
c
a
l
e

129. The following are the pulse rate of 20 subjects.
82, 48, 45, 81, 83, 74, 76, 60, 75, 79, 63, 55, 68, 46,
60, 58, 54, 51, 47, 641

80
70
60
50
PR
Box and Whisker Plot

130. GR-A
82, 48, 45, 81, 83, 74, 76, 60, 75, 79, 63, 5
5, 68, 46, 60, 58, 54, 51, 44, 58
GR-B
72 , 64 , 45 , 68 , 72 , 74 , 70 , 64 , 72 , 71
, 63 , 48 , 60 , 46 , 60 , 40 , 54 , 51 , 47
, 59
PULSE RATE

131. BA
Group
80
60
40
PR
Comparison of PR between Group-A and B

132. Score-A22225162020221823192520253839
Score-B43025162020241823142525253830
The following table shows the score on certain test for two groups.

133. Column Bar Error Diagram
•Itisusedforthepresentationofquantitative
typeofdata.Itisusefulincomparingmeanand
S.E.( )oftwoormoregroups.
•Intherectangularaxessystem,alongoneofthe
axisscaleismarkedwhileotheraxisistakenas
baselineorguideline.
•Foreachgroupunderstudybarisprojected
fromthebaselineproportionaltothemeanand
thewhiskers(lines)aredrawnfromtheendof
thebartothevaluesmeanS.E.n
DS..
Error Standard 

134. Serum cholesterol by BMI status
Group Sample sizeMean
(mg/dl)
SD
(mg/dl)S.E.
BMI>25 258 191.2 31.11 1.937
BMI<=25 283 184.7 26.96 1.603

135. Error Bar Diagram
95% C.I.
Group n Mean S.D.S.E.Mean -1.96*S.E.Mean+1.96*S.E
1 30 50.40 5.250.96 48.52 52.28
2 30 53.37 4.580.84 51.73 55.01
The graph depicts 95% C I for weight for group 1 and 2.

136. Exercise / Examples
of
Tables and Graphs

137. DR IRFAN MOMIN

139. DR IRFAN MOMIN
Infant Mortality Rates
6
11
27
64
79
65
5757

140. DR IRFAN MOMIN
Infant Mortality Rate79
85
56
68
73
47
57
62
42
Urban Rural Total
NFHS-1NFHS-2NFHS-3

141. 1735
397
139
212
1487
203
127
62
648
837580
181
0
250
500
750
1000
1250
1500
1750
19981999200020012002200320042005200620072008200920102011* WPV1 cases, India, 1998 -2011
Year
* data as on 2 March 2012

142. DR IRFAN MOMIN45
50
54
28
53
NFHS-1NFHS-2NFHS-3 Urban Rural
Marital Status
Percent of women age 20-24 married by
age 18
NFHS-3

143. DR IRFAN MOMIN
Desire for No More Children among
Women with 2 Children9083
72
88
76
66
61
47
37
NFHS-1 NFHS-2 NFHS-3
2 sons 1 son and 1 daughter2 daughters

144. DR IRFAN MOMIN
Trends in Child Nutritional Status40
23
45
43
20
51
UnderweightWastedStunted
NFHS-3NFHS-2
Percent of children age under 3 years
(Low-height-
for-age)
(Low-weight-
for-height)
(Low-weight-for-
age)

145. DR IRFAN MOMIN
Anaemiaamong Children74
79
81
72
Total Urban Rural NFHS-2
Percent of children 6-35 months with anemia

146. DR IRFAN MOMIN
Nutritional Status of Adults
34
9
24
13
55
36
BMI below normal Overweight/Obese Anaemic
Women Men
Percent of women and men age
15-49

147. DR IRFAN MOMIN
Malnutrition of Women by Residence and
Education36
25
41 42
35 35
25
36
13
24
7
7
13 14
21
11
0
10
20
30
40
50
60
Tot al Ur ba n Ru ra l
No ed ucat ion
< 8 y ear s 8- 9 year s 10+ year s
NF HS- 2 to tal
UnderweightOverweight
Percent of women age 15-49

148. DR IRFAN MOMIN
Malnutrition of Men by Residence and
Education34
8
27
14
38
5
40
3
38
5
40
6
25
14
0
5
10
15
20
25
30
35
40
45
50
Total Urban Rural
No education
< 8 years 8-9 years 10+ years
Overweight
Underweight
Percent of men age 15-49

149. DR IRFAN MOMIN
Child Immunization Trends62
54
52
42
35
72
63
55
51
42
78
78
55
59
44
BCG
Polio3
DPT3
Measles
All Vaccines
NFHS-1NFHS-2NFHS-3
Percent of children age 12-23 months
vaccinated

150. Urban and Rural Population in Gujarat in
2011
DR IRFAN MOMIN

151. Major causes of deathin childrenunder5 with
disease-specificcontribution of undernutrition
DR IRFAN MOMIN

152. State wise Contribution
New Leprosy Cases -year 2007-08Uttar Pradesh,
22.54,
23%
Bihar,
13.83,
14%
West Bengal,
9.84,
10%
Maharashtra,
9,
9%
Andhra Pradesh,
7.3,
7%
Chhattisgarh,
5.67,
6%
Gujarat,
5.25,
5%
Jharkhand,
.94, 5%
Madhya Pradesh,
4.4,
4%
Tamilnadu,
4,
4%
Orissa,
4.13,
4%
Karnataka,
3.28,
3%
Delhi,
0.97,
1%
Others,
4.85,
5%
Contribution by six states 20.8% pop & 34.5% new cases
DR IRFAN MOMIN

154. DR IRFAN MOMIN

155. Trends in Global Deaths 2002-30
Source: World Health Statistics 2007DR IRFAN MOMIN

156. DR IRFAN MOMIN

157. Trend of Leprosy Prevalence & Annual
New Case Detection (ANCDR) Rates0.740.720.84
1.3
25.9
20.0
13.7
10.9
8.4
5.95.8
5.5
4.2
3.7
5.35.3
2.4
3.2
1.17
1.2
1.42.3
5.9
5.5
7.0
8.9
5.65.14.6
4.95.7
6.4
6.25.9
4.4
3.3
0
5
10
15
20
25
30
1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
Year (March End)
Prevalence & ANCDR
PR
ANCDR
DR IRFAN MOMIN

158. The dist of Serum Cholesterol Levels of patients
with IHD.
Serum Cholesterol Levels
(mg/dl)
Number %
150-160 10 8.55
160-170 15
12.82
170-180 13
11.11
180-190 16
13.68
190-200 19
16.24
200-210 22
18.80
210-220 22
18.80
Total 117
100.00

159. Freq dist of Pulse Rate by Sex
Sex
Pulse Rate Male
No(%)
Female
No(%)
Total
No(%)
30 –35 05(55.56)04(44.44)09(100)
35 –40 09(42.86)12(57.14) 21(100)
40 –45 13(56.52)10(43.48) 23(100)
45 –50 18(54.55) 15(45.45) 33(100)
50 –55 15(51.72) 14(48.28) 29(100)
55 –60 11(45.83) 13(54.17) 24(100)
60 –65 08(61.54)05(38.46)13(100)
65 –70 07(53.85)06(46.15)13(100)
70 –75 05(55.56)04(44.44)09(100)
75 –80 03(75.00)01(25.00)04(100)
Total 94(52.81) 84(47.19)178(100)

160. Freq dist of Weight by SBP
SBP
Weight<= 140 mmHg > 140 mmHg
50 –55 15 1
55 –60 20 3
60 –65 28 5
65 –70 10 6
70 –75 8 10
75 –80 6 11

161. Nevirapine
Child with HIV
Yes No Total
Yes 9 71 80
No 45 75 120
Total 54 146 200
Exposure
Outcome
Yes No Total
Yes a b a+b
No c d c+d
Total a+c b+d a+b+c+d
Case-control study or Cohort study

162. Table-2:
AN OVERVIEW OF
PATTERN OF SUSPECTED
CASES OF LEPTOSPIROSIS
IN SOUTH GUJARAT
DURING LAST 15 YEARS
YearCasesDeaths No. of
Affected Villages
1996 40 9 37
1997 659 76 344
1998 537 42 295
1999 365 32 251
2000 156 16 124
2001 4 0 4
2002 58 2 50
2003 371 57 291
2004 630 92 364
2005 390 80 254
2006 270 78 199
2007 523 133 351
2008 566 124 374
2009 224 55 196
2010 633 127 396

163. Table-3: Month wise Suspected cases of
Leptospirosis 2004-2010
Month 2004 2005 2006 2007 2008 2009 2010
Jan 9 0 2 0 0 0 0
Feb 7 0 2 0 0 0 0
Mar 2 0 1 0 0 0 0
Apr 0 0 3 0 0 0 0
May 5 0 4 0 0 0 0
Jun 4 0 0 0 4 0 2
Jul 54 41 54 60 62 20 29
Aug 398 159 102 194 264 103 184
Sep 75 127 79 197 175 93 351
Oct 8 73 11 69 47 6 59
Nov 2 1 6 3 0 0 0
Dec 4 0 4 0 0 0 0

164. Table-4: District wise distribution of Leptospirosis
cases in 2010
Dist
Suspected
cases
Deaths
among
Suspected
cases
Confirmed
cases
Deaths
Among
Confirmed
cases
VALSAD 106 25 67 10
NAVSARI 197 27 135 16
SURAT 193 48 133 36
TAPI 112 22 72 12
SMC 9 1 4 1
Others 16 4 9 3
Total 633 127 420 78

165. District Block Cases Deaths District Block Cases Deaths
VALSAD Dharampur
14 2
SURAT Bardoli
40 6
Kaprada 2 1 Choriyasi 8 1
Pardi 28 10 Kamrej 15 5
Umergam 8 3 Mahuva 33 8
Valsad 51 9 Mandvi 35 13
ValsadCity
3 0
Mangrol
2 1
NAVSARI Chikhli
79 14 Olpad 6 2
Gandevi 38 5 Palsana 53 11
Jalalpore 32 1 Umarpada 1 1
NavsariCity
42 6
TAPI Songadh
9 3
Vansda 6 1 Uchhal 6 2
Valod 19 2
Vyara 78 15
Table-5: Block wise Cases of Leptospirosis and
Deaths due to Leptospirosis-2010

166. Age
Group
(Years)
Suspected
cases
Deaths
among
Suspected
cases
Cured
among
Susp.
cases
0-14 5 0 5
15-25 114 20 94
26-45 334 67 267
46-65 168 39 129
>=66 12 1 11
Table-6: Age wise Distribution of Suspected
Cases and Deaths due to Leptosirosis-2010

167. Table-7: Sex wise Distribution of Suspected Cases,
Deaths and Cured among Leptosirosis-2010
Male Female Total
Susp cases 435 198 633
Deaths among
Susp
cases
90 37 127
Cured among
Susp
cases
345 161 506

168. KEEP SMILING
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