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 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 that can drive the predictions.

The dependent variable is also popular as a predictor, response, and endogenous variable while the independent variable is known as an explanatory, regressor, and exogenous variable.

There are basically two kinds of regression analysis: Simple regression and multiple regression.

Simple regression uses a single explanatory variable and multiple regression uses many numbers 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.

Equation for simple regression:

Y= a+bX

Where, Y= Dependent variable

X= Independent(Explanatory) variable

a= Intercept, b= Slop

Equation for Multiple regression:

Y= a+bX1+cX2+dX3+eX4+…..

Where, Y= Dependent variable

X1, X2, X3, X4= Independent (Explanatory) variables

a= Intercept, b,c,d= Slops

Regression analysis includes variables like Linear regression and Non-linear regression.

Linear regression is basically a straight-line relationship and non-linear are the ones which have a 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 of Regression Analysis:

  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 relationships.
  3. Helps in predicting the dependent variable value from the independent variable values.
  4. The 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.


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