Regression Analysis is one of the most widely used tools in business analysis. It is the process of analyzing the relationship between variables. Basically if there are two variables, the variable that acts as the basis of estimation is called as the independent variable and the variable whose value is to be estimated is known as the dependent variable. It is used to derive a response variable as a result of one or more variables which can drive the predictions.

The dependent variable is also popular as predictor, response and endogenous variable while the independent variable is known as explanatory, regressor and exogenous variable. There are basically two kinds of regression analysis: Simple regression and multiple regression.

Simple regression uses single explanatory variable and multiple regression uses many number of explanatory variables. Once the results are derived it will further help in predicting the dependent variable when the independent variable is a known factor and this is done with the help of regression statistics.

Linear regression are basically straight-line relationship and non-linear are the ones which have curved relationship. The data used in this method can be cross-sectional where data is collected from the same time period and time series in which data collected is observed only at specific points in the same time.

### Uses:

1. It helps in devising a functional relationship between two variables.
2. It is one of the widely used tools in economic and business research where statistical interpretations are highly valued as their analysis is based more on cause and effect relationship.
3. Helps in predicting the dependent variable value from the independent variable values.
4. Coefficient of correlation and coefficient of determination can be established with the help of regression coefficients.

### Procedure for selecting variables:

• Step-wise regression
• Forward selection
• Backward elimination

Get more definitions about Regression analysis and other ERP related terms here.