1. Statistics
Ms. K. Lavanya
MSc(N)-CHN
Associate Professor

2. Definition:
• Statistics is a branch of mathematics dealing with the collection, analysis,
interpretation, presentation, and organization of data.
• Vital statistics- ongoing collection by government agencies relating to vital events such
as births, marriages, divorce, disease related states and events etc., which are deemed
reportable by local health authorities.
• Bio Statistics can be defined as an art and science of collection, compilation,
presentation, analysis and logical interpretation of biological data affected by
multiplicity of factors.

3. Uses of statistics:
• Vital statistics shows changing pattern of the population of a country. ( Medical
facilities, Standard of living, etc).
• Manufacturers plan their production by using vital statistics ( food, clothing etc).
• Birth & death certificates are needed in many circumstances
• Accurately registered and systematically collected vital statistics can be used to check
up the accuracy of data provided by the census.
• The vital statistics are the administrative and research needs of public health
agencies.

4. Purposes:
• Community Health
• Administrative purpose
• Health programmed organization
• Legislative purpose
• Governmental purpose

5. Principal Application of statistics
 Population estimation & forecasting
 Surveys of population characteristics, health needs and problems
 Analysis of health trends
 Epidemiologic research
 Programme evaluation
 Programme planning
 Budget preparation and justification
 Operational and administrative decision making
 Health Education

6. Importance of Statistics
• Statistics executes the work simply and gives a transparent picture of the work
we do regularly.
• The statistical methods help us to examine different areas such as medicine,
business, economics, social science and others.
• Statistics equips us with different kinds of organized data with the help of graphs,
tables, diagrams and charts.
• Statistics helps to understand the variability of the data pattern in a quantitative
way
• Statistics makes us understand the bulk of data in a simple way
• Statistics is the way to collecting accurate quantitative data

7. • Types of Statistics in Maths
Statistics have majorly categorized into two types:
1.Descriptive statistics
2.Inferential statistics

8. Descriptive Statistics
In this type of statistics, the data is summarised through the given observations.
The summarisation is one from a sample of population using parameters such as
the mean or standard deviation.
Descriptive statistics is a way to organise, represent and describe a collection of
data using tables, graphs, and summary measures. For example, the collection
of people in a city using the internet or using Television.
Descriptive statistics are also categorised into four different categories:
• Measure of frequency
• Measure of dispersion
• Measure of central tendency
• Measure of position

9. Inferential Statistics
• This type of statistics is used to interpret the meaning of Descriptive statistics.
• That means once the data has been collected, analysed and summarised then we
use these stats to describe the meaning of the collected data.
• It is used to draw conclusions from the data that depends on random variations
such as observational errors, sampling variation, etc.
• Inferential Statistics is a method that allows us to use information collected from a
sample to make decisions, predictions or inferences from a population. It grants us
permission to give statements that goes beyond the available data or information.
For example, deriving estimates from hypothetical research.

10. Stages of Statistical Investigation
Collection
Organization
Presentation
Analysis
Interpretation

11. 1. Collection:
The collection of statistical data is one of the most important aspects of
a statistical inquiry. In this stage, you collect relevant data from multiple
sources – both primary and secondary in nature.

12. 2. Organization:
• The second important stage is the collection and presentation of
numerical data.
• The collected data need to edit, classify, and tabulate the data in
order to organize it. Editing data involves the removal of omissions,
inaccuracies, and inconsistencies present in the data.

13. 3. Presentation:
• Presenting the organized data in the form of Diagrams & Graphs.
4. Analysis:
• Once the data is collected, organized, and presented, it is important to analyze the numerical data in order to get a better
understanding of the subject matter. You can use some popular measures to analyze numerical data like:
• Averages or measures of the central tendency
• Dispersion
• Correlation
• Skewness
• Regression
• Association and Attributes
• Interpolation and Extrapolation, etc.

14. 5. Interpretation
• Once you have analyzed the numerical data, you must draw
conclusions and inferences from it. This is the interpretation of
numerical data.

15. Importance of Vital Statistics:
Statistics are important because today we live in the information
world and much of this information’s are determined mathematically
by Statistics Help.
The role of statistics and indeed of statisticians in national
development is very critical.
Statistics has played an increasingly important role in science, in
industry, in health and in business.
 For the past few decades, people have increasingly learned to
collect and use data, perform designed experiments, to use
evidence instead of intuition to make decisions.

16. Importance of Vital Statistics:
With the computer and network revolutions, data is now easy to collect and
store. And with modern software, it is comparatively easy to analyze, even
when you have a lot of data.
The National Statistics Day (NSD) is celebrated every year in India on June
29 to create public awareness about importance of statistics in socio-
economic planning and policy.

17. Scales of Measurement
Measurement scale, in statistical analysis, the type of information provided
by numbers.
Each of the four scales (i.e., nominal, ordinal, interval, and ratio) provides a
different type of information.

18. Levels:

19. Stevens (1946) classified variables into 4 levels

21. Nominal:
• Nominal scales are naming scales
• They represent categories where there is no basis for ordering the categories.

22. Ordinal:
• Ordinal scales involve categories that can be ordered along a pre-established
dimension.
• We state the latter property by saying that we do not have equal intervals
between the items.
• Rankings also represent ordinal scales because we know the order but do not
know how different each person is from the next person.

23. Interval:
• Interval scales are very similar to standard numbering scales except they do
not have a true zero.
• That means that the distance between successive numbers is equal, but that
the number zero does NOT mean that there is none of the property being
measured.
• Many measures that involve psychological scales, especially those that use a
form of normal standardization(e.g., IQ), are assumed to be interval scales
of measurement.

24. Ratio
• Ratio scales are the easiest to understand because they are numbers as we
usually think of them.
• The distance between adjacent numbers is equal on a ratio scale and the
score of zero on the ratio scale means that there is none of whatever is being
measured.
• Most ratio scales are counts of things

26. Functions of Measurement:
Measurements is widely used for
• Selection of personnel in industry/ institution
• Various types of classification effectively
• In order to compare two individuals
• In research, it is basic part
• Improving classroom instruction

27. Nominal Scale
• A nominal scale is the 1st level of measurement scale in which the
numbers serve as “tags” or “labels” to classify or identify the
objects. A nominal scale usually deals with the non-numeric
variables or the numbers that do not have any value.

28. Characteristics of Nominal Scale
• A nominal scale variable is classified into two or more categories. In
this measurement mechanism, the answer should fall into either of
the classes.
• It is qualitative. The numbers are used here to identify the objects.
• The numbers don’t define the object characteristics. The only
permissible aspect of numbers in the nominal scale is “counting.”

29. • Example:
An example of a nominal scale measurement is given below:
• What is your gender?
• M- Male
• F- Female
• Here, the variables are used as tags, and the answer to this question should be
either M or F.

30. Ordinal Scale
• The ordinal scale is the 2nd level of measurement that reports the
ordering and ranking of data without establishing the degree of
variation between them. Ordinal represents the “order.” Ordinal
data is known as qualitative data or categorical data. It can be
grouped, named and also ranked

31. Characteristics of the Ordinal Scale
• The ordinal scale shows the relative ranking of the variables
• It identifies and describes the magnitude of a variable
• Along with the information provided by the nominal scale, ordinal
scales give the rankings of those variables
• The interval properties are not known
• The surveyors can quickly analyse the degree of agreement
concerning the identified order of variables

32. • Example:
• Ranking of school students – 1st, 2nd, 3rd, etc.
• Ratings in restaurants
• Evaluating the frequency of occurrences
• Very often
• Often
• Not often
• Not at all
• Assessing the degree of agreement
• Totally agree
• Agree
• Neutral
• Disagree
• Totally disagree

33. Interval Scale
• The interval scale is the 3rd level of measurement scale. It is
defined as a quantitative measurement scale in which the
difference between the two variables is meaningful. In other words,
the variables are measured in an exact manner, not as in a relative
way in which the presence of zero is arbitrary.

34. Characteristics of Interval Scale:
• The interval scale is quantitative as it can quantify the difference
between the values
• It allows calculating the mean and median of the variables
• To understand the difference between the variables, you can
subtract the values between the variables
• The interval scale is the preferred scale in Statistics as it helps to
assign any numerical values to arbitrary assessment such as
feelings, calendar types, etc.

35. • Example:
• Likert Scale
• Net Promoter Score (NPS)
• Bipolar Matrix Table

36. Ratio Scale
• The ratio scale is the 4th level of measurement scale, which is
quantitative. It is a type of variable measurement scale. It allows
researchers to compare the differences or intervals. The ratio scale
has a unique feature. It possesses the character of the origin or
zero points.

37. Characteristics of Ratio Scale:
• Ratio scale has a feature of absolute zero
• It doesn’t have negative numbers, because of its zero-point feature
• It affords unique opportunities for statistical analysis. The variables
can be orderly added, subtracted, multiplied, divided. Mean,
median, and mode can be calculated using the ratio scale.
• Ratio scale has unique and useful properties. One such feature is
that it allows unit conversions like kilogram – calories, gram –
calories, etc.

38. • Example:
An example of a ratio scale is:
• What is your weight in Kgs?
• Less than 55 kgs
• 55 – 75 kgs
• 76 – 85 kgs
• 86 – 95 kgs
• More than 95 kgs