Page 11 - DMGT209_QUANTITATIVE_TECHNIQUES_II

Quantitative Techniques-II

Notes Descriptive Statistics

There are statistical methods which are used for re-arranging, grouping and summarising sets
of data to obtain better information of facts and thereby better description of the situation that
can be made.

Example: Changes in the price-index. Yield by wheat etc. are frequently illustrated
using different types of charts and graphs.
These devices summarise large quantitatives of numerical data for easy understanding. Various
types of averages can also reduce a large mass of data to a single descriptive number. The
descriptive statistics include the methods of collection and presentation of data, measure of
Central tendency and dispersion, trends, ‘index numbers, etc.

Inductive Statistics

It is concerned with the development of some criteria which can be used to derive information
about the nature of the members of entire groups (also called population or universe) from the
nature of the small portion (also called sample) of the given group. The specific values of the
population members are called ‘parameters’ and that of sample are called ‘statistics’. Thus,
inductive statistics is concerned with estimating population parameters from the sample statistics
and deriving a statistical inference.

Samples are drawn instead of a complete enumeration for the following reasons:
 The number of units in the population may not be known.
 The population units may be too many in number and/or widely dispersed. Thus complete
enumeration is extremely time consuming and at the end of a full enumeration so much
time is lost that the data becomes obsolete by that time.
 It may be too expensive to include each population item.

Inductive statistics, includes the methods like:probability and probability distributions; sampling

and sampling distributions; various methods of testing hypothesis; correlation, regression,
factor analysis; time series analysis.
Statistical Decision Theory

Statistical decision theory deals with analysing complex business problems with alternative
courses of action (or strategies) and possible consequences. Basically, it is to provide more
concrete information concerning these consequences, so that best course of action can be identified
from alternative courses of action.
Statistical decision theory relies heavily not only upon the nature of the problem on hand, but
also upon the decision environment. Basically there are four different states of decision
environment as given below:

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