Biostatistics PowerPoint Presentation...

Introduction To Biostatistics
 Key words
 Statistics , data , Biostatistics,
 Variable ,Population ,Sample

Introduction To Some Basic concepts
 Statistics is a field of study concerned with
 1- collection, organization, summarization and
analysis of data.
 2- drawing of inferences about a body of data
when only a part of the data is observed.
 Statisticians try to interpret and
 communicate the results to others.

Biostatistics
 The tools of statistics are employed in many
fields
 business, education, psychology, agriculture,
economics, etc.
 When the data analyzed are derived from the
biological science and medicine,
 we use the term biostatistics to distinguish this
particular application of statistical tools and
concepts.

Data
 The raw material of Statistics is data.
 We may define data as figures. Figures result
from the process of counting or from taking a
measurement.
 For example
 - When a hospital administrator counts the number
of patients (counting).
 - When a nurse weighs a patient (measurement)

Sources of Data
 We search for suitable data to serve as the raw
material for our investigation.
 Such data are available from one or more of the
following sources
 1- Routinely kept records.
 For example
 - Hospital medical records contain immense
amounts of information on patients.
 Hospital accounting records contain a wealth of
data on the facilitys business
• activities

2- External sources.
 The data needed to answer a question may already
exist in the form of
 published reports, commercially available data
banks, or the research literature, i.e. someone
else has already asked the same question.
3- Surveys
 The source may be a survey, if the data needed is
about answering certain questions.
• For example

 If the administrator of a clinic wishes to obtain
information regarding the mode of transportation
used by patients to visit the clinic,
 then a survey may be conducted among
 patients to obtain this information.

4- Experiments.
 Frequently the data needed to answer
 a question are available only as the
 result of an experiment.
 For example
 If a nurse wishes to know which of several
strategies is best for maximizing patient
compliance,
 she might conduct an experiment in which the
different strategies of motivating compliance
 are tried with different patients.

A variable
 It is a characteristic that takes on different
values in different persons, places, or things.
 For example
 - heart rate,
 - the heights of adult males,
 - the weights of preschool children,
 - the ages of patients seen in a dental clinic.

Quantitative Variables
 It can be measured in the usual sense.
 For example
 - the heights of adult males,
 - the weights of preschool children,
 the ages of patients seen in a
 dental clinic.

Qualitative Variables
 Many characteristics are not capable of being
measured. Some of them can be ordered or ranked.
 For example
 - classification of people into socio-economic
groups,
 - social classes based on income, education, etc.

A discrete variable
 is characterized by gaps or interruptions in the
values that it can assume.
 For example
 The number of daily admissions to a general
hospital,
 The number of decayed, missing or filled teeth
per child in an elementary school.

A continuous variable
 can assume any value within a specified relevant
interval of values assumed by the variable.
 For example
 Height,
 weight,
 skull circumference.
 No matter how close together the observed heights
of two people, we can find another person whose
height falls somewhere in between.

A population
 It is the largest collection of values of a
random variable for which we have an interest at
a particular time.
 For example
 The weights of all the children enrolled in a
certain elementary school.
 Populations may be finite or infinite.

A sample
 It is a part of a population.
 For example
 The weights of only a fraction of these children.

Types of Data
•Constant Data
 These are observations that remain the same from
person to person, from time to time, or from
place to place.
 Examples
 1- number of eyes, fingers, ears etc.
 2- number of minutes in an hour
 3- the speed of light
 4- no. of centimeters in an inch

VARIABLE DATA 1
 These are observations, which vary from one
person to another or from one group of members to
others and are classified as following
 Statistically
 Quantitative variable data
 Qualitative variable data
 Epidemiologically
 Dependant (outcome variable)
 Independent (study variables)
 Clinically
 Measured (BP, Lab. parameters, etc.)
 Counted (Pulse rate, resp. rate, etc.)
 Observed (Jaundice, pallor, wound infection)
 Subjective (headache, colic, etc.)

VARIABLE DATA 2
 Statistically, variable could be
 - Quantitative variable
 a- Continuous quantitative
 b- Discrete quantitative
 - Qualitative variable
 a- Nominal qualitative
 b- Ordinal qualitative

VARIABLE DATA 3
 1- Quantitative variable
 These may be continuous or discrete.
 a- Continuous quantitative variable
 Which are obtained by measurement and its value
could be integer or fractionated value.
 Examples Weight, height, Hgb, age, volume of
urine.
 b-Discrete quantitative variable
 Which are obtained by enumeration and its value
is always integer value.
 Examples Pulse, family size, number of live
births.

Continuous Variable
Continuous Discrete Variables
• 0
3
2
1
-2
-1
-3

• Discrete Variable
0
1
2
3
22

VARIABLE DATA 4
 2- Qualitative variable
 Which are expressed in quality and cannot be
enumerated or measured but can be categorized
only.
 They can be ordinal or nominal.
 a- Nominal qualitative can not be put in order,
and is further subdivided into dichotomous (e.g.
sex, male/female and Yes/No variables) and
multichotomous (e.g. blood groups, A, B, AB, O).
 b- Ordinal qualitative can be put in order. e.g.
degree of success, level of education, stage of
disease.

VARIABLE DATA 5
 Epidemiologically, variable could be
 Dependent Variable
 Usually the health outcome(s) that you are
studying.
 Independent Variables
 Risk factors, casual factors, experimental
treatment, and other relevant factors. They also
termed predictors.
 e.g. Cancer lung is the dependent variable
while smoking is independent variable.

Descriptive Statistics: Measures
of Central Tendency
 key words
 Descriptive Statistic, measure of
central tendency ,statistic, parameter, mean (µ)
,median, mode.

The Statistic and The Parameter
 A Statistic
 It is a descriptive measure computed from the
data of a sample.
 A Parameter
 It is a descriptive measure computed from the
data of a population.
 Since it is difficult to measure a parameter from
the population, a sample is drawn of size n,
whose values are 1,2 , , n. From this
data, we measure the statistic.

Measures of Central Tendency
 A measure of central tendency is a measure which
indicates where the middle of the data is.
 The three most commonly used measures of central
tendency are
 The Mean, the Median, and the Mode.
 The Mean
 It is the average of the data.

The Median
 When ordering the data, it is the observation
that divide the set of observations into two
equal parts such that half of the data are before
it and the other are after it.
 If n is odd, the median will be the middle of
observations. It will be the (n1)/2 th ordered
observation.
 When n 11, then the median is the 6th
observation.
 If n is even, there are two middle
observations. The median will be the mean of
these two middle observations. It will be the
(n1)/2 th ordered observation.
• When n 12, then the median is the 6.5th
observation, which is an observation halfway
between the 6th and 7th ordered observation

Example

 For the same random sample, the ordered
observations will be as
 23, 28, 28, 31, 32, 34, 37, 42, 50, 61.
 Since n 10, then the median is the 5.5th
observation, i.e. (32+34)/2= 33.

Properties of the Median
 Uniqueness. For a given set of data there is one
and only one mean.
 Simplicity. It is easy to understand and to
compute.
 Affected by extreme values. Since all values
enter into the computation.

Example
 Assume the values are 115, 110, 119,
117, 121 and 126. The mean is118.
• But assume that the values are 75, 75, 80, 80 and
280. The mean is also 118, a value that is not
representative of the set of data as a whole

The Mode
 It is the value which occurs most frequently.
 If all values are different there is no mode.
 Sometimes, there are more than one mode.
 Example
 For the same random sample, the value 28 is
repeated two times, so it is the mode.
 Properties of the Mode
 Sometimes, it is not unique.
 It may be used for describing qualitative data.

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