This document provides an overview of business statistics. It defines statistics as a branch of mathematics that deals with collecting, organizing, analyzing, and interpreting numerical data to make effective inferences. Statistics can be descriptive, summarizing data, or inferential, using samples to estimate characteristics of populations. The key types of data are primary, collected directly, and secondary, already existing data collected by others. Data is classified and can be presented in tables, diagrams, and graphs for analysis using measures of central tendency, dispersion, skewness, kurtosis, correlation, regression, and multivariate techniques. Inferential statistics helps estimate population parameters from sample statistics and test hypotheses.

1. Business Statistics

2. Meaning of Statistics
Statistics in considered as a branch of mathematics.
In 1791 Sir John Sinclair introduced the term ‘Statistics’ in English.
Statistics has been generally used in two senses (1) Plural Sense (2) Singular Sense
Plural Sense: Refers to numerical statements of facts relating to any field of inquiry such as numerical
data related to production, income, expenditure, price, etc.
Singular Sense: Refers to science in which we deal with tools and techniques for collecting, classifying,
presenting, analyzing, and interpreting the data.

3. Definition of Statistics
Webster’s Third New International Dictionary gives a comprehensive definition of statistics as a
science dealing with the collection, organization, analysis, and interpretation of numerical data to
make an effective inference for the final decision.
According to an English Statistician Tippet “Statistics is both a science and an art.” It is a science
as its methods are systematic and has general applications. It is an art as its successful applications
depend on the skills and special experience of a statistician.

4. Branches of Statistics
The study of statistics can be organized in a variety of ways. One of the main ways is to subdivide
statistics into two branches: descriptive statistics and inferential statistics.
1. Descriptive Statistics
2. Inferential Statistics

5. Descriptive Statistics
As the name suggests, descriptive statistics merely describe the numerical data and consist of the
tools and techniques used in the collection, organization, presentation, and analysis of data in order to
describe the various features and characteristics of such data.

6. Data
The term data as used in statistics means quantitative data only.
Qualitative aspects such as intelligence, honesty, and good or bad have no significance in statistics
until or unless these are assigned some numerical value. To assign the numerical value to qualitative
data, we have four types of data measurement levels.
i. Nominal Scale
ii. Ordinal Scale
iii. Interval Scale
iv. Ratio Scale
.

7. Types of Data
Statistical data are mainly two types (i) Primary Data (ii) Secondary Data
Primary Data: Data collected by the investigator for his own purpose, for the first time from
beginning to end, is called primary data.
Secondary Data: Secondary data are those which are already in existence, and which have been
collected by others for some other purpose than answering the question in hand.

8. The distinction between primary and secondary
Data.
1. Difference in originality
2. The difference in the suitability of objectives
3. The difference in the cost of data collection

9. Collection of Data
Methods for collecting primary data
Primary data may be collected by using any of the following methods:
i. Direct Personal Interview
ii. Indirect Oral Investigation
iii. Mailed Questionnaire Method
iv. Schedules sent through Enumerators
Methods for collecting secondary data
Secondary data may be collected by using any of these two sources:
i. Published source
ii. Unpublished source
.

10. Classification of Data
Classification is a process of arranging the data into different groups or classes according to some
common characteristics. We can classify the data based on the characteristics such as
i. Geographical Classification
ii. Chronological Classification
iii. Qualitative Classification
iv. Quantitative Classification
Quantitative classification is the most popular method of classifying data. Variables such as Discrete,
Continuous, Uni-variate, Bi-variate variables, and different types of frequency distributions come
under the quantitative classification.
.

11. Presentation of Data
The presentation of data means the exhibition of the data in such a clear and attractive manner that
are easily understandable and conclusions are drawn effectively. We can present the data in the forms
of:
i. Table
ii. Diagram
iii. Graph
.

12. Analysis of Data
To summarise and describe the Uni-variate data, we can use descriptive statistics such as:
i. Measure of Central Tendency
ii. Dispersion
iii. Skewness
iv. Kurtosis
Whereas, to establish the relationship in bi-variate data, we can use
i. Linear Correlation Analysis
ii. Linear Regression Analysis
In the same way, to establish the relationship in multi-variate data, we can use
i. Multiple Correlation Analysis
ii. Multiple Regression Analysis
Multivariate Analysis

13. Inferential Statistics
It deals with the methods which help in estimating the characteristics of a population or making
decisions concerning a population based on the sample statistics. To be brief, inferential statistics
help the decision maker to conclude the characteristics of a large population based on sample results.
Population and sample are the two relative terms.

14. Population Parameter Vs Sample Statistics
A population consists of all the items or individuals about which you want to draw a conclusion
and the measured characteristics of a population are called population parameters.
A fraction or segment of the population is known as a sample and the measured characteristics of
the sample are called sample statistics.

16. In inferential statistics, we study two major activities
i. Estimation of the unknown parameter of the population based on sample statistics.
ii. Testing whether the sample data have sufficient evidence to support or reject a hypothesis
about the population parameter.

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