1. Introductory Statistics
Data: Qualitative and Quantitative
Statistics: Deals with quantitative data.
(or Numerical data.)
Definition: It is the scientific method of
collection, classification, presentation ,
analysis and decision making of the
quantitative data

2. History
• In India, Kautilya’s ‘Arthashastra’ contains
Statistics of Agriculture, metals, animal
husbandry, medicine etc. during the period
of Chandragupta Maurya.
• Similarly Ain-e-Akbari gives account of
statistics relating to population, production,
land revenue during Akbar’s Rule.

3. Phases of statistical study
A typical problem requiring use of statistical study
involves the following steps:
• Defining objective (s)
• Specification of population and its characteristic(s)
• Planning for collection of data.
• Collection of data.
• Compilation/Presentation of data.
• Statistical Analysis
• Drawing conclusions, testing assumptions or
making predictions

4. Scope of statistics
• Simplifying complex data.
• Conversion of data into information and
make it more useful in decision making.
• Quantifies and measures uncertainty and
variability and so helps in measuring risk.
• Discovers past and emerging patterns in a
data. It helps in forecasting.
• Helps in estimation and validating
assumptions

5. Applications of statistics in various
areas
Marketing:
Philip Kotler and Gary Armstrong state:
“ identifying customer needs and wants,
determines which target markets the
organizations can serve best and designs
appropriate products, services and programs to
serve these markets.”
Statistical methods helps in forecasting sales,
market share and demand for various types of
industrial products

6. Applications of statistics
Economics:
formulation of economic policies, econometrics
Finance:
helps in value at risk, stock market-derivative
Insurance:
based on concept of probability
Operations:
inventory, SQC, six sigma method

7. Applications of statistics
HR: performance evaluations,
Feedback of training program
IT: Optimization of server time,
Testing software
Data mining:
specialized branch of combination of IT &
statistics
It is used in all fields of business

8. Some statistical techniques and
their applications
Technique field specific
applications
Binomial
Distribution
quality
assurance
sampling
inspection
Cluster analysis marketing
planning
target
marketing
Correlation
Regression
financial risk
marketing
cross market
analysis
Index Numbers Economics Wholesale and
consumer price
index

9. Some statistical techniques and
their applications
Normal
distribution
equity research
finance
marketing
production engg
project
management
risk
management
performance
appraisal
six sigma
PERT/CPM
Sampling market
research
consumer
survey

10. Some statistical techniques and
their applications
Testing of
Hypothesis
Agriculture,
paramedical,
Pharmaceutical
testing a
fertilizer, testing
a drug, testing
of drug, clinical
trial
Decision theory Finance Investment and
portfolio
selection

11. Some statistical techniques and
their applications
Forecasting HRD
Insurance
Marketing
manpower
planning
Determining
premium
Demand
forecasting
Discriminant
Analysis
Finance,
Marketing
Credit risk
analysis,
Customer
profile

12. Limitations of statistics
It does play very important role as discussed
but it has to be implemented by well defined
objectives, scientific collection of data,
appropriate assumptions and analysis.
Scarcity of any of the above leads to wrong
conclusion