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INTRODUCTION TO BIOSTATISTICS

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Title: INTRODUCTION TO BIOSTATISTICS


1
INTRODUCTION TO BIOSTATISTICS
  • Dr. Zafar Mahmood
  • zafarjan90_at_yahoo.com
  • 0346-9079308

2
This session covers
  • Background and need to know Biostatistics
  • Origin and development of Biostatistics
  • Definition of Statistics and Biostatistics
  • Types of data
  • Graphical representation of a data
  • Frequency distribution of a data

3
  • Statistics is the science which deals with
    collection, classification and tabulation of
    numerical facts as the basis for explanation,
    description and comparison of phenomenon.
  • ------ Lovitt

4
BIOSTATISICS
  • (1) Statistics arising out of biological
    sciences, particularly from the fields of
    Medicine and public health.
  • (2) The methods used in dealing with statistics
    in the fields of medicine, biology and public
    health for planning, conducting and analyzing
    data which arise in investigations of these
    branches.

5
Main Branches of Biostatistics
  • Descriptive Biostatistics
  • Methods of producing quantitative summaries of
    information in biological sciences
  • Tabulation and graphical presentations
  • Measures of central tendency
  • Measures of dispersion

6
Branches of Biostatistics
  • Inferential Biostatistics
  • Methods of making generalizations about a larger
    group based on information about a subset
    (sample) of that group in biological sciences
  • Estimation
  • Testing of hypothesis

7
Populations and Samples
  • Before we can determine what statistical tools
    and technique to use, we need to know if our
    information represents a population or a sample
  • A sample is a subset which should be
    representative of a population

8
Samples
  • A sample should be representative if selected
    randomly (i.e., each data point should have the
    same chance for selection as every other point)
  • In some cases, the sample may be stratified but
    then randomized within the strata

9
Example
  • We want a sample that will reflect a populations
    gender and age
  • Stratify the data by gender
  • Within each strata, further stratify by age
  • Select randomly within each gender/age strata so
    that the number selected will be proportional to
    that of the population

10
Population
  • The totality of all the observation whether
    finite or infinite in any field of interest is
    called population
  • Example
  • Total number of patients in HMC

11
Parameter and Statistic
  • Parameter Summary value or characteristic of
    population or universe
  • Statistic Summary value or characteristic of
    sample used for making inferences about parameter

12
Origin and development of statistics in Medical
Research
  • In 1929 a huge paper on application of statistics
    was published in Physiology Journal by Dunn.
  • In 1937, 15 articles on statistical methods by
    Austin Bradford Hill, were published in book
    form.
  • In 1948, a RCT of Streptomycin for pulmonary tb.,
    was published in which Bradford Hill has a key
    influence.
  • Then the growth of Statistics in Medicine from
    1952 was a 8-fold increase by 1982.

13
C.R. Rao
Ronald Fisher
Karl Pearson
Douglas Altman
Gauss -
14
  • Basis of Biostatistics

15
Sources of Medical Uncertainties
  1. Intrinsic due to biological, environmental and
    sampling factors
  2. Natural variation among methods, observers,
    instruments etc.
  3. Errors in measurement or assessment or errors in
    knowledge
  4. Incomplete knowledge

16
Intrinsic variation as a source of medical
uncertainties
  • Biological due to age, gender, heredity, parity,
    height, weight, etc. Also due to variation in
    anatomical, physiological and biochemical
    parameters
  • Environmental due to nutrition, smoking,
    pollution, facilities of water and sanitation,
    road traffic, legislation, stress and strains
    etc.,
  • Sampling fluctuations because the entire world
    cannot be studied and at least future cases can
    never be included
  • Chance variation due to unknown or complex to
    comprehend factors

17
Natural variation despite best care as a source
of uncertainties
  • In assessment of any medical parameter
  • Due to partial compliance by the patients
  • Due to incomplete information in conditions such
    as the patient in coma

18
Medical Errors that cause Uncertainties
  • Carelessness of the providers such as physicians,
    surgeons, nursing staff, radiographers and
    pharmacists.
  • Errors in methods such as in using incorrect
    quantity or quality of chemicals and reagents,
    misinterpretation of ECG, using inappropriate
    diagnostic tools, misrecording of information
    etc.
  • Instrument error due to use of non-standardized
    or faulty instrument and improper use of a right
    instrument.
  • Not collecting full information
  • Inconsistent response by the patients or other
    subjects under evaluation

19
Incomplete knowledge as a source of Uncertainties
  • Diagnostic, therapeutic and prognostic
    uncertainties due to lack of knowledge
  • Predictive uncertainties such as in survival
    duration of a patient of cancer
  • Other uncertainties such as how to measure
    positive health

20
  • Biostatistics is the science that helps in
    managing medical uncertainties

21
Reasons to know about biostatistics
  • Medicine is becoming increasingly quantitative.
  • The planning, conduct and interpretation of much
    of medical research are becoming increasingly
    reliant on the statistical methodology.
  • Statistics pass through the medical literature.

22
CLINICAL MEDICINE
  • Documentation of medical history of diseases.
  • Planning and conduct of clinical studies.
  • Evaluating the merits of different procedures.
  • In providing methods for definition of normal
    and abnormal.

23
Role of Biostatistics in patient care
  • In increasing awareness regarding diagnostic,
    therapeutic and prognostic uncertainties and
    providing rules of probability to delineate those
    uncertainties
  • In providing methods to integrate chances with
    value judgments that could be most beneficial to
    patient
  • In providing methods such as sensitivity-specifici
    ty and predictivities that help choose valid
    tests for patient assessment
  • In providing tools such as scoring system and
    expert system that can help reduce epistemic
    uncertainties

24
PREVENTIVE MEDICINE
  • To provide the magnitude of any health problem
    in the community.
  • To find out the basic factors underlying the
    ill-health.
  • To evaluate the health programs which was
    introduced in the community (success/failure).
  • To introduce and promote health legislation.

25
Role of Biostatics in Health Planning and
Evaluation
  • In carrying out a valid and reliable health
    situation analysis, including in proper
    summarization and interpretation of data.
  • In proper evaluation of the achievements and
    failures of a health programme

26
Role of Biostatistics in Medical Research
  • In developing a research design that can minimize
    the impact of uncertainties
  • In assessing reliability and validity of tools
    and instruments to collect the infromation
  • In proper analysis of data

27
Example Evaluation of Penicillin (treatment A)
vs Penicillin Chloramphenicol (treatment B) for
treating bacterial pneumonia in childrenlt 2 yrs.
  • What is the sample size needed to demonstrate the
    significance of one group against other ?
  • Is treatment A is better than treatment B or
    vice versa ?
  • If so, how much better ?
  • What is the normal variation in clinical
    measurement ? (mild, moderate severe) ?
  • How reliable and valid is the measurement ?
    (clinical radiological) ?
  • What is the magnitude and effect of laboratory
    and technical
  • error ?
  • How does one interpret abnormal values ?

28
WHAT DOES BIO- STAISTICS COVER ?
  • Planning
  • Design
  • Execution (Data
    collection)
  • Data Processing
  • Data analysis
  • Presentation
  • Interpretation
  • Publication

29
BASIC CONCEPTS
Data Set of values of one or more variables
recorded on one or more observational units
Sources of data 1. Routinely kept
records 2. Surveys (census) 3.
Experiments 4. External source
Categories of data 1. Primary data
observation, questionnaire, record form,
interviews, survey, 2. Secondary data census,
medical record, registry etc
30
TYPES OF DATA or VARIABLE
  • QUALITATIVE DATA
  • DISCRETE QUANTITATIVE
  • CONTINOUS QUANTITATIVE

31
Qualitative Data or Variable
  • a variable or characteristic which cannot be
    measured in quantitative form but can only be
    identified by name or categories, for instance
    place of birth, ethnic group, type of drug,
    stages of breast cancer (I, II, III, or IV),
    degree of pain (minimal,
  • moderate, severe or unbearable).

32
Quantitative Data or Variable
  • A quantitative variable is one that can be
    measured and expressed numerically and they can
    be of two types (discrete or continuous).
  • The values of a discrete variable are usually
    whole numbers, such as the number of episodes of
    diarrhea in the first five years of life.
  • A continuous variable is a measurement on a
    continuous scale. Examples include weight,
    height, blood pressure, age, etc.

33
TYPES OF DATA or VARIABLE
  • Although the types of variables could be broadly
    divided into categorical (qualitative) and
    quantitative , it has been a common practice to
    see four basic types of data (scales of
    measurement).
  • Nominal, Ordinal, Interval Ratio data

34
Qualitative Nominal data
  • Data that represent categories or names. There is
    no implied order to the categories of nominal
    data. In these types of data, individuals are
    simply placed in the proper category or group,
    and the number in each category is counted. Each
    item must fit into exactly one
  • category.
  • The simplest data consist of unordered,
    dichotomous, or "either - or types of
    observations, i.e., either the patient lives or
    the patient dies, either he has some particular
    attribute or he does not.

35
Nominal scale data Example
  • survival status of propanolol - treated and
  • control patients with myocardial infarction

Status 28 days after hospital admission Propanolol -treated patient Control Patients
Dead 7 17
Alive 38 29
Total Survival rate 45 84 46 63
36
Some other examples of nominal data
  • Example Sex ( M, F)
  • Exam result (P, F)
  • Blood Group (A,B, O or AB)
  • Color of Eyes (blue, green,
  • brown,
    black)
  • Anemia's ( Microcytic, Macrocytic
  • Religion - Christianity, Islam, Hinduism, etc

37
Qualitative Ordinal data
  • The ordinal scale data have order among the
    response classifications (categories). The spaces
    or intervals between the categories are not
    necessarily equal.
  • It is similar to nominal b/c the measurement
    involve categories, however, the categories are
    ordered by rank.

38
Ordinal Scale data Examples
  • Pain level (Mild, Moderate, Severe)
  • Tumors (Stage 0, , IV)
  • Arthritis (Class 1, , 4 )
  • Military Rank (Lt., Capt., Maj., Col., General)

39
Some other examples of ordinal data
  • Response to treatment
  • (poor, fair, good)
  • Severity of disease
  • (mild, moderate, severe)
  • Income status
  • (low, middle, high)

40
  • QUANTITATIVE (DISCRETE)
  • Example The no. of family members
  • The no. of heart beats
  • The no. of admissions in a day
  • QUANTITATIVE (CONTINOUS)
  • Example Height, Weight, Age, BP, Serum
    Cholesterol and BMI

41
Discrete data -- Gaps between possible values
Number of Children
Continuous data -- Theoretically, no gaps between
possible values
Hb
42
  • CONTINUOUS DATA
  • QUALITATIVE DATA
  • wt. (in Kg.) under wt, normal over wt.
  • Ht. (in cm.) short, medium tall

43
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44
Scale of measurement
Qualitative variable A categorical
variable Nominal (classificatory) scale  -
gender, marital status, race Ordinal (ranking)
scale  - severity scale, good/better/best
45
Quantitative Variable
Quantitative variable A numerical variable
discrete continuous
Numerical discrete data occur when the
observations are integers that correspond with a
count of some sort. Some common examples are the
number of bacteria colonies on a plate, the
number of cells within a prescribed area upon
microscopic examination, the number of heart
beats within a specified time interval, a
mothers history of number of births ( parity)
and pregnancies (gravidity), the number of
episodes of illness a patient experiences during
some time period, etc.
46
Quantitative Variable..
Numerical continuous The scale with the greatest
degree of quantification is a numerical
continuous scale. Each observation theoretically
falls somewhere along a continuum (range). One is
not restricted, in principle, to particular
values such as the integers of the discrete
scale. The restricting factor is the degree of
accuracy of the measuring instrument most
clinical measurements, such as blood pressure,
serum cholesterol level, height, weight, age etc.
are on a numerical continuous scale.
47
Quantitative Interval Scale of measurement
Quantitative variable A numerical variable
discrete continuous Interval scale Data is
placed in meaningful intervals and order. The
unit of measurement are arbitrary. There is no
true zero - Temperature (37º C -- 36º C 38º
C-- 37º C are equal) and No implication of
ratio (30º C is not twice as hot as 15º C)
48
Quantitative Ratio Scale of measurement
  • Data is presented in frequency distribution
    in logical order. A meaningful ratio exists.
    There is a true zero
  • - Age, weight, height, pulse rate
  • - pulse rate of 120 is twice as fast as 60
  • - person with weight of 80kg is twice as heavy
    as the one with weight of 40 kg.

49
Scales of Measure
  • Nominal qualitative classification of equal
    value gender, race, color, city
  • Ordinal - qualitative classification which can
    be rank ordered socioeconomic status of
    families
  • Interval - Numerical or quantitative data can
    be rank ordered and sizes compared temperature
  • Ratio - Quantitative interval data along with
    ratio time, age.

50
CLINIMETRICS
  • A science called clinimetrics in which qualities
    are converted to meaningful quantities by using
    the scoring system.
  • Examples (1) Dummy score based on appearance,
    pulse, grimace, activity and respiration is used
    for neonatal prognosis.
  • (2) Smoking Index no. of cigarettes, duration,
    filter or not, whether pipe, cigar etc.,
  • (3) APACHE( Acute Physiology and Chronic Health
    Evaluation) score to quantify the severity of
    condition of a patient

51
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54
INVESTIGATION
55
Methods Of Data Collection, Organization And
Presentation
Learning Objectives
  • Identify the different methods of data
    organization and
    presentation
  • 2. Understand the criterion for the selection of
    a method to organize and present data
  • 3. Identify the different methods of data
    collection and criterion that we use to select a
    method of data collection
  • 4. Define a questionnaire, identify the different
    parts of a
  • questionnaire and indicate the procedures to
    prepare a
  • questionnaire

56
Data Collection Methods
Various data collection techniques can be used
such as Observation Face-to-face and
self-administered interviews Postal or mail
method and telephone interviews Using available
information Focus group discussions (FGD)
Other data collection techniques Rapid
appraisal techniques, 3L technique, Nominal group
techniques, Delphi techniques, life histories,
case studies, etc.
57
Observation
Observation is a technique that involves
systematically selecting, watching and recoding
behaviors of people or other phenomena and
aspects of the setting in which they occur, for
the purpose of getting (gaining) specified
information. It includes all methods from simple
visual observations to the use of high level
machines and measurements, sophisticated
equipment or facilities, such as radiographic,
biochemical, X-ray machines, microscope, clinical
examinations, and microbiological examinations
58
Interviews and self-administered questionnaire
Interviews and self-administered questionnaires
are probably the most commonly used research data
collection techniques. Therefore, designing good
questioning tools forms an important and
time consuming phase in the development of most
research proposals. Once the decision has been
made to use these techniques, the following
questions should be considered before designing
our tools
59
Interviews and self-administered questionnaire..
1. What exactly do we want to know, according to
the objectives and variables we identified
earlier? Is questioning the right technique to
obtain all answers, or do we need additional
techniques, such as observations or analysis of
records?
2. Of whom will we ask questions and what
techniques will we use? Do we understand the
topic sufficiently to design a questionnaire, or
do we need some loosely structured interviews
with key informants or a focus group discussion
first to orient ourselves?
60
Interviews and self-administered questionnaire..
  • Are our informants mainly literate or illiterate?
    If illiterate, the use of self-administered
    questionnaires is not an option.
  • 4. How large is the sample that will be
    interviewed? Studies with many respondents often
    use shorter, highly structured questionnaires,
    whereas smaller studies allow more flexibility
    and may use questionnaires with a number of
    open-ended
  • questions.

61
Face-to-face and telephone interviews
Face-to-face and telephone interviews have many
advantages A good interviewer can stimulate and
maintain the respondents interest, and can
create a rapport (understanding, concord) and
atmosphere conducive to the answering of
questions. If anxiety aroused, the interviewer
can allay it. If a question is not understood an
interviewer can repeat it and if necessary (and
in accordance with guidelines decided in advance)
provide an explanation or alternative wording.
In face-to-face interviews, observations can be
made as well.
62
Mailed Questionnaire Method
Under this method, the investigator prepares a
questionnaire containing a number of questions
pertaining the field of inquiry. The
questionnaires are sent by post to the informants
together with a polite covering letter explaining
the detail, the aims and objectives of collecting
the information, and requesting the respondents
to cooperate by furnishing the correct replies
and returning the questionnaire duly filled in.
In order to ensure quick response, the return
postage expenses are usually borne by the
investigator.
63
Use of documentary sources
Clinical and other personal records, death
certificates, published mortality statistics,
census publications, etc. are documentary
sources. Examples include 1. Official
publications of Central Statistical Authority 2.
Publication of Ministry of Health and Other
Ministries 3. News Papers and Journals. 4.
International Publications like Publications by
WHO, World Bank, UNICEF 5. Records of hospitals
or any Health Institutions.
64
Problems in gathering data
  • Common problems might include
  • Language barriers
  • Lack of adequate time
  • Expense
  • Inadequately trained and experienced staff
  • Invasion of privacy
  • Suspicion
  • Bias (spatial, project, person, season,
    diplomatic, professional)
  • Cultural norms (e.g. which may preclude men
    interviewing
  • women)

65
Choosing a Method of Data Collection
Decision-makers (consultants ) need information
that is relevant, timely, accurate and usable.
The cost of obtaining, processing and analyzing
these data is high. The challenge is to find
ways, which lead to information that is
cost-effective, relevant, timely and important
for immediate use. Some methods pay attention
to timeliness and reduction in cost. Others pay
attention to accuracy and the strength of the
method in using scientific approaches.
66
Categories of Data
Primary Data are those data, which are collected
by the investigator himself for the purpose of a
specific inquiry or study. Such data are original
in character and are mostly generated by surveys
conducted by individuals or research
institutions. The first hand information obtained
by the investigator is more reliable and accurate
since the investigator can extract the correct
information by removing doubts, if any, in the
minds of the respondents regarding certain
questions.
67
Categories of Data.
Secondary Data When an investigator uses data,
which have already been collected by others, such
data are called "Secondary Data". Such data are
primary data for the agency that collected them,
and become secondary for someone else who uses
these data for his own purposes. The secondary
data can be obtained from journals,
reports, government publications, publications of
professionals and research organizations.
68
Types of Questions
Before examining the steps in designing a
questionnaire, we need to review the types of
questions used in questionnaires. Depending
on how questions are asked and recorded we can
distinguish two major possibilities 1. Open
ended questions, 2. Closed questions.
69
Open-ended questions
Open-ended questions permit free responses that
should be recorded in the respondents own words.
The respondent is not given any possible answers
to choose from.
For example Can you describe exactly what the
traditional birth attendant did when your labor
started? What do you think are the reasons for
a high drop-out rate of village health committee
members? What would you do if you noticed that
your daughter (school girl) had a problem in
education?
70
Closed Questions
  • Closed questions offer a list of possible options
    or answers from which the respondents must
    choose. When designing closed questions one
    should try to
  • Offer a list of options that are exhaustive and
    mutually
  • Exclusive
  • Keep the number of options as few as possible.

For example What is your marital status? 1.
Single 2. Married/living together 3.
Separated/divorced/widowed
71
Closed Questions.
Closed questions may also be used if one is only
interested in certain aspects of an issue and
does not want to waste the time of the respondent
and interviewer by obtaining more information
than one needs.
  • For example, a researcher who is only interested
    in the protein content of a family diet may ask
  • Did you eat any of the following foods
    yesterday? (Circle yes or no for each set of
    items)
  • Peas, bean, lentils Yes No
  • Fish or meat Yes No
  • Eggs Yes No
  • Milk or Cheese Yes No

72
Designing the Questionnaire
Steps involved in designing the
questionnaire 1) Content Take your Objectives
and Variable Decide measure of quantitative
variables or levels of qualitative variables to
reach your objectives 2) Formulating
Questions Questions need to be clearly worded
so as not to confuse the respondent or arouse
extraneous attitudes. Questions should provide
a clear understanding of the information
sought Be precise avoid ambiguity and wording
that might be perceived to elicit a specific
purpose. Questions may be open-ended, multiple
choice, completion or variations of
these. Studiously avoid overly complex questions
73
3) Sequencing Questions Sequence of questions
should be informant friendly beginning with a
natural conversation questions (e.g., age,
marital status, education etc) Restrict
yourself to an essential minimum questions while
asking personal information Start then with
interesting but non-controversial questions At
the end pose more sensitive questions 4) Formattin
g the Questionnaire Provide a separate page
explaining the purpose of the study, requesting
the informant consent to be interviewed and
assuring confidentiality of the data
recorded. Each questionnaire has must have
heading and space locating SNO., data and
location of the interviewer. Sufficient space
is provided for answer to open-ended
questions. Proper and attractive layout
5) Translation The interview will be conducted
in one or more local languages and should be
translated to the original language for
standardizing the questions.
74
Key Principle for Constructing a Questionnaire
1) It should be easy for the respondent to read,
understand, and answer. 2) Motivate the
respondents to answer 3) Be designed for
efficient data processing 4) Have a well
designed professional appearance 5) Design to
minimize missing data
75
Frequency Distributions
  • data distribution pattern of variability.
  • the center of a distribution
  • the ranges
  • the shapes
  • simple frequency distributions
  • grouped frequency distributions
  • midpoint

76
Tabulate the hemoglobin values of 30 adult male
patients listed below
Patient No Hb (g/dl) Patient No Hb (g/dl) Patient No Hb (g/dl)
1 12.0 11 11.2 21 14.9
2 11.9 12 13.6 22 12.2
3 11.5 13 10.8 23 12.2
4 14.2 14 12.3 24 11.4
5 12.3 15 12.3 25 10.7
6 13.0 16 15.7 26 12.5
7 10.5 17 12.6 27 11.8
8 12.8 18 9.1 28 15.1
9 13.2 19 12.9 29 13.4
10 11.2 20 14.6 30 13.1
77
Steps for making a table
  • Step1 Find Minimum (9.1) Maximum (15.7)
  • Step2 Calculate difference 15.7 9.1 6.6
  • Step3 Decide the number and width of
  • the classes (7 c.l) 9.0 -9.9,
    10.0-10.9,----
  • Step4 Prepare dummy table
  • Hb (g/dl), Tally mark, No. patients

78
 
DUMMY TABLE
Tall Marks TABLE
   
79
Table Frequency distribution of 30 adult male
patients by Hb
80
Table Frequency distribution of adult patients
by Hb and gender
81
Elements of a Table
Ideal table should have Number
Title Column headings
Foot-notes Number Table number
for identification in a report Title,place
- Describe the body of the table,
variables, Time period (What, how
classified, where and when) Column -
Variable name, No. , Percentages (),
etc., Heading Foot-note(s) - to describe some
column/row headings, special cells,
source, etc.,
82
Table II. Distribution of 120 Corporation
divisions according to annual death rate based on
registered deaths in 1975 and 1976
Figures in parentheses indicate percentages
83
DIAGRAMS/GRAPHS
  • Discrete data
  • --- Bar charts (one or two groups)
  • Continuous data
  • --- Histogram
  • --- Frequency polygon (curve)
  • --- Stem-and leaf plot
  • --- Box-and-whisker plot

84
Example data
68 63 42 27 30 36 28 32 79 27 22 28 24 25 44 65
43 25 74 51 36 42 28 31 28 25 45 12 57 51 12 3
2 49 38 42 27 31 50 38 21 16 24 64 47 23 22 43
27 49 28 23 19 11 52 46 31 30 43 49 12
85
Histogram
Figure 1 Histogram of ages of 60 subjects
86
Polygon
87
Example data
68 63 42 27 30 36 28 32 79 27 22 28 24 25 44 65
43 25 74 51 36 42 28 31 28 25 45 12 57 51 12 3
2 49 38 42 27 31 50 38 21 16 24 64 47 23 22 43
27 49 28 23 19 11 52 46 31 30 43 49 12
88
Stem and leaf plot
Stem-and-leaf of Age N 60 Leaf Unit
1.0 6 1 122269 19 2
1223344555777788888 (11) 3 00111226688 13
4 2223334567999 5 5 01127 4 6
3458 2 7 49
89
Box plot
90
Descriptive statistics report Boxplot
  • - minimum score
  • maximum score
  • lower quartile
  • upper quartile
  • median
  • - mean
  • the skew of the distribution positive
    skew mean gt median high-score whisker is
    longer negative skew mean lt median
    low-score whisker is longer

91
Pie Chart
  • Circular diagram total -100
  • Divided into segments each representing a
    category
  • Decide adjacent category
  • The amount for each category is proportional to
    slice of the pie

The prevalence of different degree of
Hypertension in the population
92
Bar Graphs
Heights of the bar indicates frequency Frequency
in the Y axis and categories of variable in the X
axis The bars should be of equal width and no
touching the other bars
The distribution of risk factor among cases with
Cardio vascular Diseases
93
HIV cases enrolment in USA by gender
Bar chart
94
HIV cases Enrollment in USA by gender
Stocked bar chart
95
Graphic Presentation of Data
the frequency polygon (quantitative data)
the histogram (quantitative data)
the bar graph (qualitative data)
96
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97
General rules for designing graphs
  • A graph should have a self-explanatory legend
  • A graph should help reader to understand data
  • Axis labeled, units of measurement indicated
  • Scales important. Start with zero (otherwise //
    break)
  • Avoid graphs with three-dimensional impression,
    it may be misleading (reader visualize less easily

98
Exercise
  • Identify the type of data (nominal, ordinal,
    interval and ratio) represented by each of the
    following. Confirm your answers by giving your
    own examples.
  • 1. Blood group
  • 2. Temperature (Celsius)
  • 3. Ethnic group
  • 4. Job satisfaction index (1-5)
  • 5. Number of heart attacks

99
Exercise ....
  • 6. Calendar year
  • 7. Serum uric acid (mg/100ml)
  • 8. Number of accidents in 3 - year period
  • 9. Number of cases of each reportable disease
    reported by a health worker
  • 10. The average weight gain of 6 1-year old dogs
    (with a special diet supplement) was 950grams
    last month.

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