Regression Analysis
Regression analysis is a powerful technique for studying relationship
between dependent variables (i.e., output, performance measure) and independent
variables (i.e., inputs, factors, decision variables). Summarizing relationships
among the variables by the most appropriate equation (i.e., modelling) allows
us to predict or identify the most influential factors and study their impacts
on the output for any changes in their current values. Unlike the deterministic
decision-making process, such as linear optimization by solving systems of
equations, Parametric systems of equations and in decision making under pure
uncertainty, the variables are often more numerous and more
difficult to measure and control.
However, the steps are the same.
They are:
1. Simplification
2. Building a decision model
3. Testing the model
4. Using the model to find the solution:
- It is a simplified
representation of the actual situation
- It need not be complete or
exact in all respects
- It concentrates on the most
essential relationships and ignores the less
essential ones.
- It is more easily understood
than the empirical (i.e., observed)
Situation, and hence permits the problem to be solved more readily
With minimum time and effort.
5. It can be used again and again for similar problems or can be
modified. Fortunately the probabilistic and statistical methods for analysis
and decision making under uncertainty are more numerous and powerful today than
ever before. The computer makes possible many practical applications. A few
examples of business applications are the following:
- An auditor can use random
sampling techniques to audit the accounts
receivable for
clients.
- A plant manager can use
statistical quality control techniques to assure the
quality of his
production with a minimum of testing or inspection.
- A financial analyst may use
regression and correlation to help understand the
relationship of a
financial ratio to a set of other variables in business.
- A market researcher may use
test of significance to accept or reject the
Hypotheses about a
group of buyers to which the firm wishes to sell a
particular product.
- A sales manager may use
statistical techniques to forecast sales for the
coming year
