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Presentation of Anthropology (Presentation of Anthropology (Presentation of Anthropology (Presentation of Anthropology (Presentation of Anthropology (Presentation of Anthropology (

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BIOSTATITCS
For Nursing students
By Dejene S.
Assistant Professor (MPH/EPI)
Email: [email protected]
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Course Contents
Biostatistics
Introduction to Biostatics
Descriptive statistics
Demography and vital statistics
Probability and probability distributions
Sampling techniques
Statistical inferences
Sample size determination

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Learning objectives
•After completing this chapter, the student will be able to:
-Define Statistics and Biostatistics
-Enumerate the importance and limitations of statistics
-Define and Identify the different types of data
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CHAPTER ONE
Introduction to Biostatistics
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i.Statistics: It is an applied science which deals with the systematic
collection, organization, presentation, analysis, interpretation, and
drawing conclusion from a data.
ii.Biostatistics: When the data are driven from the biological sciences and
medicine, we use the term biostatistics.
• Biostatistics is an ART of conducting a study, analyzing the data, and derive
useful conclusions from numerical outcomes about real life problems…
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Definition of terms:
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•Provide methods of organizing information
•Resource allocation (planning)
•Essential for scientific method of investigation
•Information from sample to population
•Essential for understanding, appraisal and critique of scientifi
c literature
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Uses of biostatistics:
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Research Planning
Design
Data collection
Data Processing
Data Analysis
Presentation
Interpretation
Publication
Biostatistical thinking
contribute in every step
in a research
The best way to learn
about biostatistics is to
follow the flow of a
research from inception
to the final publication
What does biostatistics cover?
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•Statistics has increasingly become an important scientific application in
medical and public health practices.

•This is because, evidence- based practice is concerned with the treatment
and prevention of disease and promotion of health.

•This evidence-based approach requires both the gathering of evidence
(information) and its critical interpretation.

•Health professionals of all categories therefore, require necessary
knowledge and skills to practice of research and to evaluate the researches
carried out.
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The Scope and Rationale:
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• Statistics appears in the form of aggregate. A single number or fact,
therefore, cannot constitute statistics.

•Statistics is enumerated according to reasonable standards of accuracy.

•Statistics must be collected in a systematic manner for a predetermined
purpose.

•They must be comparable. Numerical facts may be placed in relation to
each other either in point of time, space or condition so that the facts are
compared.
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Characteristics of Statistics:
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•It measures only quantitative aspects that can be expressed
numerically.

•It doesn’t deal with individual facts. A single fact cannot be
considered statistics.

•Statistical data are true only on an average.

•Can be misused
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Limitations of statistics:
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1. Descriptive biostatistics:
•Helps to identify the general features and trends in a set of data and ext
racting useful information. Ways of organizing and summarizing data
Includes:
Data collection, Organization, Summarization & Presentation of data

•Descriptive statistics include:
 Tables, Bar chart, Pie chart, Scatter plot, etc.
Numerical summary measures
 Measures of central tendency (location)
 Measures of variability (dispersion)
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Types of Biostatistics
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2. Inferential biostatistics:
•Methods used for drawing conclusions about a population based on
the information obtained from a sample.


•Study to what extent data from the sample can be generalized to a
general (infinite) population

Includes:
•hypothesis testing, determining relationships making inferences,
making predictions, etc.
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Types of Biostat cont.
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•Data , Variable ,
•Population ,Sample

Data:
•The raw material of Statistics is data.
•Data consist of information coming from observations, counts, measurements,
or responses.


•For example:
- When a hospital administrator counts the number of patients (counting)
- When a nurse weighs a patient (measurement)
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Key terms
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•It is a characteristic that takes different values in different persons,
places, or things.

•A variable is any characteristics, number, or quantity that can be
measured or counted.

For example:
- day temperature,
- the heights of adult males,
- the weights of preschool children,
- the ages of patients seen in a dental clinic.
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Variable
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Quantitative Variables
•It can be measured in the usual
sense.
Quantitative - Measurable or
Countable:
Examples are-
- number of students in a class
- height of an individual
- Age of an individual
Qualitative Variables
•Many characteristics are not capable of
being measured. Some of them can be
ordered or ranked.
Qualitative - Categorical or Nominal:
Examples: - Color, Gender, Race
- Blood group, eye color,
- Marital status, educational level, etc
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Types of variables
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A discrete variable
•is characterized by gaps or
interruptions in the values that it can
assume.
•the values of a discrete variable are
usually whole numbers.

For example:
-E.g. the number of heart beats within
a specified time interval,

-the number of episodes of diarrhea a
child experiences in a year
A continuous variable
•Can assume any value within a
specified interval.

•For example:
-Height, weight, Age
-skull circumference

•No matter how close together the
observed heights of two people, we
can find another person whose height
falls somewhere in between.
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Types of quantitative variables
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•A population: is the largest collection of individuals for which we have an
interest at a particular time. Populations may be finite or infinite.

For example:
•The weights of all the children enrolled in a certain elementary school.

A sample: It is a part of a population. It is a subset of the measurements
selected from the population, from which information is actually obtained.

For example:
•The weights of only a fraction of these children.
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Scales of measurement
•Measurement is the assignment of numbers to objects or events according
to a set of rules.

•There are four basic types scales of measurement

Nominal Scale:-
Categorized using names, labels, or qualities
•Examples: -
•Male–female, well–sick, child–adult, and married–not married.
•Religion - Christianity, Islam, Hinduism, etc
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ii. Ordinal Scale:- Whenever observations are not only different from category
to category but can be ranked according to some criterion, they are said to be
measured on an ordinal scale.

•Arranging the observations from lowest to highest, order array
•Differences between data entries is not meaningful.

Example:
•Individuals may be classified according to socioeconomic status as low, medium, or
high.
•The intelligence of children may be above average, average, or below average
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iii. Interval Scale:-
•Data can be ordered
•Differences between data entries are meaningful.
•Perhaps the best example of an interval scale is provided by the way in
which temperature is usually measured (degrees Fahrenheit or Celsius).
•E.g. the difference b/n 70
o
c and 71
o
c is the same as the difference b/n
32
o
c and 33
o
c.
•But the scale is not a RATIO Scale. 40
o
F is not twice as much as 20
o
F.

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iv. Ratio Scale:-
The data values in ratio data have true zero,
•Physical measurements of height, weight, length are typically ratio variables
•One data value can be meaningfully expressed as a multiple of another
•E.g. age is a ratio data, some one who is 40yrs old is twice as old as
someone who is 20 years old.
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Scales of measurement (summary)
•Nominal: Classifies persons or things based on a qualitative assessment
•Similar or dissimilar but not more or less
•Can be numeric but there is no implication of more or less
•Ordinal: Classifies persons or things based on a qualitative assessment
•More or less but not how much more or less
•Interval: Indicates how much more or less
•Does not contain a true zero point
•Can not create meaningful ratios of these two numbers
•Ratio: includes all characteristics of interval scale, but contains a true zero
point.
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Scale of Measurement
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Scale Classification Order Equal Intervals True zero
Nominal Yes No No No
Ordinal Yes Yes No No
Interval Yes Yes Yes No
Ratio Yes Yes Yes Yes

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Exercise
1.For each of the following variables, indicate whether it is quantitative or
qualitative and specify the measurement scale that is employed when
taking measurements on each:
a)Class standing of the members of this class relative to each other
b)Admitting diagnosis of patients admitted to a mental health clinic
c)Weights of babies born in a hospital during a year
d)Gender of babies born in a hospital during a year
e)Under-arm temperature of day-old infants born in a hospital
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2.Identify the scale described in each situation below:
•Individual Cell phone numbers,
•country telephone codes,
•Temperature of patients at a health facility
•The weight of children under five at a weekly baby weighing
•The length of time spent in the hospital
•Smoking status; smoker or non-smoker
•Attendance; present or absent
•Class mark; pass or fail
•Pulse rate
•Opinion of students about biostatistics; very happy, unhappy
neutral
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Descriptive Statistics
 Methods of Data Organization and Presentation
•Data collection
•Organization of data
•Summarization of data
•Measures of central tendency
•Measures of variability
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I. Data Collection
•Before any statistical work can be done data must be collected.
•Depending on the type of variable and the objective of the study different
data collection methods can be employed.
•In the collection of data we have to be systematic. If data are collected
haphazardly, it will be difficult to answer research questions in a
conclusive way.
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•Depending on source, it is classified in to two

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.



 It is more reliable and accurate since the investigator can extract the
correct information by removing doubts.
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- Secondary Data: When an investigator uses data, which have already been
collected by others.

•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, publications,
and research organizations.

•Secondary data are less expensive to collect both in money and time.

•Such data must be used with great care, because such data may be full of
errors.
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Data collection methods
Data collection method depends on types of variables and
objective of study.
 Interview
 Questionnaire
 Observation
 Focus group discussion
 Use of documentary sources
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1. Interview
Defn:- a technique that is primarily used to gain an understanding of the
under lining reasons and motivations for peoples’ attitude, preferences or
behavior.

 It is a way of gathering information through communication between the
interviewer and interviewees.

It could be a face to face interview or Telephone interview
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Advantages
Good response rate
 Completed and immediate
 Possible in- depth questions
 Interviewer in control and can
give help if there is a need
Disadvantages
•Time consuming
• Need to set up interviews
• Geographic limitations
• Can be expensive
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Qualities needed for the interviewer

 Proper training
 Respectful, confident and relaxed
 Amiability (friendliness, Sociability, etc )
 Neutrality
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2. Questionnaire
 Depending on how questions are asked and recorded there are two major
possibilities
•Open –ended questions, and
•closed questions.

I.Open-ended questions
 Permit free responses that should be recorded in the respondent’s own words.
 The respondent is not given any possible answers to choose from.
 Such questions are useful to obtain information on:
•Opinions, attitudes, and or Sensitive issues.

•E.g: “Can you describe exactly what the traditional birth attendant did when your
labor started?”
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II.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.

 “What is your marital status?
1.Single
2.Married/living together
3.Separated/divorced/widowed

 “Do you want to attend TB/HIV conference?”
1.Yes 2. No
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Advantages:-
• Can be used as a method in its own right or as a basis
for interviewing or a telephone survey
• Can be posted, e-mailed, or faxed
• Can cover a large number of participants,
• Relatively cheap
• No prior arrangements are needed
• No interviewer bias
• Wide geographic coverage
Disadvantages:
• Design problems
•Questions have to be
relatively simple
• Time delay for waiting
response
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Self administered questionnaire
•Is a data collection tool in which written questions are presented to be
answered by the respondent.
•Gathering all or parts of the respondents
•Giving oral or written instruction
•Letting the respondents fill out the questionnaire.

• Assume no literacy problem
• Problem with incomplete questionnaires
• Not possible to give assistance if required
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•A method that involves critical observation and recording the
practice (behavior, culture…) of individuals or a group.

•Excellent approach to discover behaviors,

•Usually takes longer time,

•Liable to “Observational bias”
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3. Observation
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4. Focus group discussion
• FGD is an interview conducted by a trained moderator with a small group of
respondent.

•Moderator- leads the discussion
•Main purpose-to gain insight by listening to a group of people from the
appropriate target .
Advantages
•Quick result and cost-effective
•Groups may generate important issues
Disadvantages
•Topic of discussion may be missed
•The discussion my be manipulated by
the moderator.
• Needs well trained professionals
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5. Documentary sources
Extraction of available of information
• Clinical and other personal records
• Death certificate
• Published mortality statistics
• Epidemic reports
• Census publications
•Official publication of central statistical Agency (CSA)
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Problems in gathering data
•Language barriers
•Lack of adequate time
•Expense
•In adequately trained and experienced staff
•Invasion of privacy
•Suspicion
•Bias
•Cultural norms


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II. Organization of data
•Numbers that have not been organized are called raw data.
•Techniques used to organize a set of data in a concise way.
•Order array
•Frequency distribution table
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Preparation of tables
Statistical table in general should have the following parts
• Table number; every table should be identified by number, it facilitate easy
reference
• Title, there should be a title at the top of every table; the title should answer
the question, what? Where and where?
• Column and Rows should be clearly labelled
• Totals should also indicated if necessary
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Frequency Distributions Tables

E.g. A teacher gave test for a class of 30 students; the mark
obtained by students are

•Ordered array: A simple arrangement of individual observations
in the order of magnitude.

•Very difficult with large sample size
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3 2 3 3 4 3 1 2 5 1
5 4 2 1 1 3 3 4 1 2
1 4 5 4 2 2 4 4 4 4
1 1 1 1 1 1 1 2 2 2
2 2 3 3 3 3 3 3 4 4
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Example: Prepare a frequency distribution for the following raw data of ages of
certain study subjects.
22 18 23 19 23 21 23 21 24 26 24
26 22 23 19 23 20 23 21 23 22 24
26 24 22 23 25 26 26 22 19 20 21
25 23 20 19 27 30 41 50 47 27 29
35 28 36 40 49 53 28 31 42 48 53
33 51 46 30 51 39 44 37 45 32 38
18 25 24 34
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Ordered Array:
18 18 19 19 19 19 20 20 20 21 21
21 21 22 22 22 22 22 23 23 23 23
23 23 23 23 23 24 24 24 24 24 25
25 25 26 26 26 26 26 27 27 28 29
30 30 31 32 33 34 35 36 37 38 39
40 41 42 43 44 45 46 47 48 49 50
51 51 53

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Frequency Distribution Table
•Is a table to summarize data
•Consists of the set of classes along with their numerical counts in each
•A table which contains the values of a variable and the corresponding freq
uencies with which each value occurs
•The actual summarization and organization of data starts from frequency distr
ibution
•The distribution summarizes the raw data into a more useful form & allows for a
quick visual interpretation of the data

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Frequency distributions for categorical variables
•Summarizing categorical variables (nominal & ordinal) is simple
•Count the number of frequency in each category & present as relative fre
quencies (percentages)
•Often presented in the form of table, bar and pie charts
•A relative frequency distribution: shows the proportion of counts that fall
into each class or category
•A relative frequency value for any category is obtained by dividing the
number of observations in that category by the total number of observatio
ns. Rf= # of observation/ # total Rf= F/N
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Frequency distributions for categorical variables
Example
•Immunization coverage in woreda X
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Sex Frequency (f) R frequency (100%)
Male 422 0.508 50.8
Female 408 0.492 49.2
Total 830 1 100
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Frequency distributions for categorical variables
Number of freshman students attending education in ArU, 2024
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Department F RF (%) CF CRF (%)
Medicine 40 0.2 40 0.2
HO 30 0.15 70 0.35
Radiology 20 0.1 90 0.45
Midwifery 50 0.25 140 0.7
Nursing 60 0.3 200 1
Total 200 1
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•Frequency: The number of observations in each class or category
•Cumulative frequencies: When frequencies of two or more classes are added.
•Relative frequencies: obtained by dividing the freq. of each class by the total
number of observations
•Percentage: Calculated by multiplying relative frequency with 100.
•Cumulative relative frequency: The proportion of the total number of observations
that have a value less than or equal to the class
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Frequency distribution for numerical variables
Quantitative variable
•A frequency distribution can also show the number of observations within certain ra
nges
•For a discrete variable, the frequencies may be tabulated either for each value
of the variable or for groups of values
•With continuous variables, groups (class intervals) have to be formed with no
n-overlapping intervals, usually of equal width.
•The first consideration is how many intervals to include
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•Frequency distributions can be used both for categorical & numerical data.
a)Continuous variable:
•In displaying numeric data (Grouping) using frequency distribution we should
note the following:
The range of values must be broken-down into a series of distinct and non-
overlapping intervals.
The intervals should cover all data points.
This facilitates comparison among classes.
The limits for each class must agree with the accuracy of the raw data.
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Age Group Frequency R Frequency (%) CR Frequency (%)
15-19 399 28.9 28.9
20-24 341 24.7 53.6
25-29 281 20.4 74.0
30-34 143 10.4 84.3
35-39 116 8.4 92.8
40-44 54 3.9 96.7
45-49 42 3.0 100.0
Total 1380 100.0
Frequency distribution of Women of Reproductive age in Asella Town, Jan
2021.

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Time
(Hours)

Frequency
Relative
Frequency(%)
C relative
Frequency (%)
10-14
15-19
20-24
25-29
30-34
35-39
5
11
12
7
3
2
0.125
0.275
0.300
0.175
0.075
0.050
0.125
0.400
0.700
0.875
0.950
1.00
Total 40 1.00
Leisure time (hours) per week for 40 college students:
23 24 18 14 20 36 24 26 23 21 16 15 19 20 22 14 13 10 19 27
29 22 38 28 34 32 23 19 21 31 16 28 19 18 12 27 15 21 25 16
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Guidelines for constructing tables
•Limit the number of variables to be included
•All tables should be self-explanatory
•Include clear title (above) telling what, when and where
•Clearly label the rows and columns
•State clearly the unit of measurement used
•Explain codes and abbreviations in the foot-note
•If data is not original, indicate the source in foot-note
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A) Simple or one-way table: used when the individual observations
involve only a single variable
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Immunization status Number Percept
Not immunized 65 33.4
Partially immunized 59 30.4
Fully immunized 70 36,0
Total 194 100.0
Example of simple /one way tables
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Two Variable Table (two way table)
Primary and secondary cases of syphilis morbidity by age and sex, 2017
Age group
(years)
Number of cases
Male Female Total
0-14
15-19
20-24
25-29
30-34
35-44
45-54
>54
40
1710
5120
5301
5537
5004
2144
1147
190
2668
5285
4306
3111
1897
487
131
230
4378
10405
9610
8648
6901
2631
1278
Total 26006 18075 44081
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•This table shows two characteristics
•is formed when either the rows or the column is divided into two or more parts.
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Marital Status
Immunization Status

Total
Immunized Non Immunized
No. % No. %
Single
Married
Divorced
Widowed
58
156
10
7
24.7
34.7
35.7
50.0
177
294
18
7
75.3
65.3
64.3
50.0
235
450
28
14
Total 231 31.8 496 68.2 727
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Example of Higher Order tables
C) Higher Order Table:
When it is desired to represent three or more characteristics in a single
table
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Profession/Sex
Residence
Urban Rural Total
Doctors
Male 180 450 630
Female 40 140 180
Nurses
Male 460 840 1,300
Female 240 460 700
Total 920 1890 2810
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Advantages of table:
•Enable the required figures to be located easily
•Enable make comparison between different categories
•Reveals patterns within the figures, which cannot be seen, in
the narrative form.
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Diagrammatic Representation
 Appropriately drawn graph allows readers to obtain rapidly overall understanding
of the data presented.
 The relationship between numbers of various magnitudes can usually be seen
more quickly and easily from a graph than from a table.

 The aim of statistical methods is to reduce the size of statistical data.
 diagrammatic representation is simpler and more easily understandable.

 It consists in presenting statistical material in geometric figures, pictures, maps
and lines or curves.
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Importance of diagrammatic representation:
•They have greater attraction than mere figures.
•They help in deriving the required information quickly without any mental strain.
•They facilitate comparison.
•They may reveal patterns in a set of data and suggest directions in which changes
are occurring.
•They have greater memorizing value than mere figures.
•When graphs are poorly designed, they not only ineffectively convey message,
but they are often misleading.
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Limitations of Diagrammatic Representation
•It is not to be used when comparison is either not possible or not
necessary.
•Diagrammatic representation is not an alternative to tabulation.
•It can give only an approximate idea and as such where greater
accuracy is needed diagrams will not be suitable.
•They fail to bring to light small differences
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Construction of graphs
General rules that are commonly accepted
•Every graph should be self-explanatory and as simple as possible.
•Titles are usually placed below the graph and it should again question
what ? Where? When? How classified?
•Legends or keys should be used to differentiate variables if more than
one is shown.
•The units in which the scale is divided should be clearly indicated.
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•The choice of the particular form among the different possibilities will
depend on personal choices and/or the type of the data.

•Specific types of graphs include:
•Bar graph
•Pie chart
•Histogram
•Stem-and-leaf plot
•Box plot
•Scatter plot
•Line graph
•Others


Nominal, ordinal data
Quantitative data
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1. Bar charts (or graphs)
•Bar diagrams are used to represent and compare the frequency distribution
of discrete variables and categorical.
•In bar diagram, all bars must have equal width and the distance between
bars must be equal.
•Categories are listed on the horizontal axis (X-axis)
•Frequencies or relative frequencies are represented on the Y-axis
•The height of each bar is proportional to the frequency of observations in
that category
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Method of constructing bar chart
• All the bars must have equal width
• The bars are not joined together (leave space between bars)
•All the bars should rest on the same line called the base
• Label both axes clearly
•There are different types of bar diagrams:
•Simple bar chart: It is a one-dimensional diagram
•The height or length of each bar indicates the frequency
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Simple bar chart
69 Bar chart for the type of ICU (intensive care unit) for 25 patients
•Used to compare two or more categories of given variable
•Only one variable is represented
•The bars are usually arranged according to relative magnitude of categories
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Distribution of undergraduate health science students of
ArU participated in a survey by year of training, June 2022
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•Bar chart of under graduate health science students of ArU
participated in a survey by year of training, June 2022
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Sub-divided bar chart
•If there are different quantities forming the sub-divisions of the
totals, simple bars may be sub-divided in the proportion of the
various sub-divisions.

•The order in which the components are shown in a “bar” is
followed in all bars used in the diagram.
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Example: Plasmodium species distribution for confirmed malaria
cases, Zeway, 2021 0
20
40
60
80
100
August October December
Percent
Mixed
P. vivax
P. falciparum
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Multiple bar chart:
Bar charts can be used to represent the relationships among two or
more variables.
• In this type of chart the component figures are shown as separate bars
adjoining each other.
• The height of each bar represents the actual value of the component figure
• The following figures show the use of multiple bar charts
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Bar chart of undergraduate health sciences students of ArU
participated in the survey by sex and year of training, June 2022
Multiple bar chart:
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There’s no reason why the bar chart can’t be plotted horizontally instead
of vertically.
0 10 20 30 40 50
CAT
Anti FGMC
Campaign
Training
Reading
HC
CHA
Type of source

Percent
female
male
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2. Pie chart
Shows the relative frequency for each category by dividing a circle into
sectors, the angles of which are proportional to the relative frequency.
Used for a single categorical variable
Use percentage distributions

Steps to construct a pie-chart
•Construct a frequency table
•Change the frequency into percentage (P)
•Change the percentages into degrees, where: degree = Percentage X 360
o
•Draw a circle and divide it accordingly
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•Vaccination status of children by card plus recall, Ethiopia, 2020
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Histogram
 Histograms are frequency distributions with continuous class intervals that
have been turned into graphs.
 To construct a histogram, we draw the interval boundaries on a horizontal line
and the frequencies on a vertical line.
 Non-overlapping intervals that cover all of the data values must be used.
 Bars are drawn over the intervals in such a way that the areas of the bars are
all proportional in the same way to their interval frequencies.
 The area of each bar is proportional to the frequency of observations
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Eg: Distribution of the age of women at the time of marriage
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Histogram for the ages of 2087 mothers with <5 children,
Adami Tulu, 2013 N1AGEMOTH
55.050.045.040.035.030.025.020.015.0
700
600
500
400
300
200
100
0
Std. Dev = 6.13
Mean = 27.6
N = 2087.00
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D. Stem-and-Leaf Plot
•A quick way to organize data to give visual impression similar to a histogram
while retaining much more detail on the data.

•Similar to histogram and serves the same purpose and reveals the presence
or absence of symmetry

•Are most effective with relatively small data sets

•Are not suitable for reports and other communications, but

•Help researchers to understand the nature of their data
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Example:
•Age of study participants: 43, 28, 34, 61, 77, 82, 22, 47, 49, 51, 29, 36, 66, 72, 41
•Order the data: 22, 28, 29, 34, 36, 41, 43, 47, 49, 51, 61, 66, 72, 77, 82
2 2 8 9
3 4 6
4 1 3 7 9
5 1
6 1 6
7 2 7
8 2
Stem and leaf plots are used as a quick way of seeing how
many pieces of data fall in various ranges. The reader can
quickly tell:
- the range
- the mode
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Steps to construct Stem-and-Leaf Plots
1.Separate each data point into a stem and leaf components
 Stem = consists of one or more of the initial digits of the
measurement
 Leaf = consists of the rightmost digit
2.Write the smallest stem in the data set in the upper left-hand corner of
the plot
3.Write the second stem (first stem +1) below the first stem
4.Continue with the remaining stems until you reach the largest stem in
the data set
5.Draw a vertical bar to the right of the column of stems
6.For each number in the data set, find the appropriate stem and write
the leaf to the right of the vertical bar
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Example:
Stem Leaf Number
30
31
32
33
34
35
36
31
01
65 60 45 00 48
23 14
84
41
49
1
1
5
2
1
1
1
BWT in g: 3031, 3101, 3265, 3260, 3245, 3200, 3248, 3323, 3314, 3484,
3541, 3649
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Frequency polygon
A frequency distribution can be portrayed graphically in yet another
way by means of a frequency polygon.

To draw a frequency polygon we connect the mid-point of the tops of
the cells of the histogram by a straight line.

The total area under the frequency polygon is equal to the area under
the histogram

Useful when comparing two or more frequency distributions by
drawing them on the same diagram

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It can be also drawn without erecting rectangles by joining the top
midpoints of the intervals representing the frequency of the
classes as follows Age of women at the time of marriage
0
5
10
15
20
25
30
35
40
12 17 22 27 32 37 42 47
Age
No of women
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Line graph
 Useful to assess the trend of particular situation overtime. Helps for monitoring
the trend of epidemics.

The time, is marked along the horizontal axis, and Values of the quantity being
studied is marked on the vertical axis.

Values for each category are connected by continuous line.

Sometimes two or more graphs are drawn on the same graph taking the same
scale, to compare.
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No. of microscopically confirmed malaria cases by species
and month at Zeway malaria control unit, 2013 0
300
600
900
1200
1500
1800
2100
JanFebMarAprMayJunJulAugSepOctNovDec
No. of confirmed malaria cases
Months
Positive
P. falciparum
P. vivax
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