Question

Explain the assumptions underlying multiple linear regression model. 

23 Mar 2023
Answer :
Word Count : 696

Multiple linear regression is a statistical technique that is used to model the relationship between multiple independent variables and a single dependent variable. The model is based on a set of assumptions that must be met for the results of the analysis to be valid. In this article, we will discuss the assumptions underlying multiple linear regression and their importance.

Assumption 1: Linearity

The first assumption underlying multiple linear regression is that the relationship between the independent variables and the dependent variable is linear. This means that the change in the dependent variable is proportional to the change in the independent variables. If this assumption is not met, then the model may not be appropriate for the data and the results may be inaccurate.

Assumption 2: Independence

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