Regression Analysis

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