Question

What are the four assumptions of linear regression?

Answer 1

Linear regression relies on five main assumptions. ... Despite its apparent simplicity, it relies however on a few key assumptions (linearity, homoscedasticity, absence of multicollinearity, independence and normality of errors). Good knowledge of these is crucial to create and improve your model.

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Answer 2

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There are four assumptions associated with a linear regression model:

1) Linearity: The relationship between X and the mean of Y is linear.

2) Homoscedasticity: The variance of residual is the same for any value of X.

3) Independence: Observations are independent of each other.

4) Multicollinearity—Multiple regression assumes that the independent variables are not highly correlated with each other.