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Introduction to Biostatistics
Dr Zia-Ul-Ain Sabiha
CHS deptt 2
Objectives of this lecture
At the end of this session the students will be able to
Define Statistics Differentiate between Descriptive
Statistics and Inferential Statistics Define Biostatistics Discuss the Uses of Biostatistics
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Cont’d
Define Data Sample and Population Variables Measurement variables Types of Data Exercises to summarize the lecture.
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STATISTICS
Statistics is science of conducting studies to collect ,organize, summarize, analyze, and draw conclusions from the data.
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Concern of Statistics
A subject that deals with the collection , compilation , presentation , analysis and presentation of data.
Drawing inferences about a body of data when only a part of the data is observed.
Scientific study of numerical data based on natural phenomenon
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Types of statistics
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Types of statistics
Descriptive Statistics Consists of the collection , organization ,
summarization, and presentation of data.
Inferential statistics consists of generalizing from samples to
populations ,performing estimations and hypothesis tests, determining relationships among variables and making predictions.
It uses probability i,e., the chance of event occuring
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HISTORICAL NOTE
The origin of descriptive Statistics can be traced to data collection methods used in censuses taken by the Babylonians and Egyptians between 4500 and 3000 B.C.
In addition the Roman Emperor Augustus (27 B.C-A.D. 17) conducted surveys on births and deaths of the citizens of the empire, as well as the number of livestock each owned and the crops each citizen harvested yearly.
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Historical Note
Inferential Statistics originated in the 1600s, when John Graunt published his book on population growth , Natural and Political Observations Made upon the Bills of Mortality.
About the same time ,another mathematician/astronomer , Edmund Hally, published the first complete mortality tables.( insurance companies use mortality tables to determine life insurance rates).
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Biostatistics
Application of Statistical methods to the solution of biological problems
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Uses of Biostatistics
In Epidemiology In demography Morbidity and mortality rates and ratios. Collection and registration of vital events.
(Births ,Deaths , Marriages and divorces) Sampling for different statistical procedures. To know the frequency of different diseases
and biological problems. For interpretation of data. To know about association and causation of
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Data
Record of observations Facts and figures Any piece of information
InformationWhen data is processed and made meaningful
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Data are numbers which can be measured or can be obtained by counting. Biostatistics is concerned with the interpretation of the data and the communication of information about the data.
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Sources of data
Data are obtained from Analysis of records Surveys Counting Experiments Reports
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Variable
A characteristics ,object and property that assumes different numerical value for each possible outcome/individual/element of the population or sample is called a variable.
The actual property measured on the individual selected for sample.
E.g. Height ,weight ,temprature,age,smoking,
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Random variables
A random variable is one that cannot be predicted in advance because it arises by chance. Observations or measurements are used to obtain the value of a random variable.
Random variables may be discrete or continuous.
Discrete random variable
A discrete random variable has gaps or interruptions in the values that it can have. The values may be whole numbers or have spaces between them.
Continuous random variable
A continuous random variable does not have gaps in the values it can assume. Its properties are like the real numbers.
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Type of variables
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Types of Variables
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Classification of variable
Independent and dependent Independent variables are presumed
causes and dependent variables are presumed effects.
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Population
The whole set of things about which we want to know.
In biostatistics population can be human beings , ECG machines , Paracetamol tablets.
A population is the collection or set of all of the values that a variable may have. A sample is a part of a population.
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Sample
A part or subset of population for actual study. OR
A collection of individual observations selected by a specific procedure from a population.
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Types of variables
Measurement variable Continuous variables Discontinuous variables or discrete
variable Ranked variables Categorical variable
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Measurement variables
Continuous variables Which at least theoretically can assume
an infinite number of values between any two points . e. g length ,period of time ,height , weight , age ,
Discrete, Discontinuous variables That has only certain fixed numerical
values with no intermediate values possible in between. e.g. number of offsprings , no. of teeth, no. of glands.
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Ranked variables
Which cannot be measured but ranked or ordered .e.g. rank order of pupa emergence or seed germination.
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Attributes variables
The characteristics that are qualitative in character and hence cannot be expressed numerically( or quantitatively) are called descriptive characteristics or attributes. The sex of a newly born baby. Eye color of a girl. The language of a Arabian visitor. The style of a speaker
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Types of Data
Data consists of variables. There are four scales of
measurement Nominal(category) Ordinal(category) Interval(quantity ,continuous ) Ratio(quantity , continuous)
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Scale Characteristic question Example
Nominal Is A different than B? Marital status Eye color Gender Religious affiliations Race
ordinal Is A bigger than B? Stage of disease Severity of pain Level of satisfaction
Interval By how many units do A and B differs?
Temperature SAT score
Ratio How many times bigger than B is A?
Distance Length Time until death Weight
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Nominal Scale
Classifies persons or things based on the characteristic being assessed
No information is given on quantity or amount
A variable without an intrinsic order. Examples Male or female Nationality (American , Mexican ,
Pakistani) Favorite pet( dog , cat, fish, snake)
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Ordinal Scale
Classifies persons or things based on the characteristic being assessed
Can be placed in meaningful order, Indicates “more than” or “less than” Does not indicate how much more or
how much less than Example: Rating a student’s
performance as ‘good’, ‘fair’, ‘poor’
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Interval Scale
Classifies persons or things based on the characteristic being assessed
Indicates “more than” or “less than ” and the magnitude of the observation
There is no true zero or meaningful zero.
Examples Temperature Year of birth Date of diagnosis
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Ratio Scale
Classifies persons or things based on the characteristic being assessed
Indicates “more than” or “less than” and the magnitude of the observation
Zero point is indicated True ratios exist when the same variable is
measured on the different members f the population.
Examples: Height Weight Age 9/6/2015
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Identify what type of variable it is.
Date of diagnosis Town of residence Age (years) Sex
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Exercise
Date of diagnosis: Interval Town of residence: Nominal Age (years): Ratio Sex : Nominal
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Exercise
Name the scale of measurement for each variable below:
Year of birth: Marital status of a women: Identification number of study
participants: Class rank: Length of infants at ANC clinic:
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Exercise
Year of birth: Interval Marital status of a women: Nominal Identification number : Nominal Class rank: Ordinal Length/Height of infants at ANC
clinic: Ratio
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Summary
Identify whether the following data is discrete or continuous
Distance from primary health center to reference lab:
Number of times a child under 5 has experienced fever in the last month:
Number of fatal accidents on a road over the past year:
Weight gained or lost by a 9 month old in the past 3 months:
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Exercise
Distance from primary health center to reference lab: Continuous
Number of times a child under 5 has experienced fever in the last month: Discrete
Number of fatal accidents on a road over the past year: Discrete
Weight gained or lost by a 9 month old in the past 3 months: Continuous
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