A brief overview of “Multiple Linear Regression”

Multiple linear regression (MLR), it is also known as multiple regression, is a statistical technique that uses several explanatory or independent variables to predict the outcome of a response or dependent variable.


The goal of multiple linear regression (MLR) is to model the linear relationship between the explanatory (independent) variables and response (dependent) variable.


Multiple regressions are based on the assumption that there is a linear relationship between both the dependent and independent variables. It also assumes no major correlation between the independent variables.


Let us look at some examples of two or more variables in MLR:


1) The house price (Dependent variable Y) depends on the various Independent variables (X) like the square footage of the house, locality, number of bedrooms, number of bathrooms, age of the house.

2) Job performance (Dependent variable Y) of an employee depends upon the various Independent variables (X) like motivation, social support, intelligence, amount of work an employee has.



Summary:

Multiple regression is an extension of linear regression models that allow predictions of systems with multiple independent variables. It does this by simply adding more terms to the linear regression equation, with each term representing the impact of a different physical parameter.

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