Question

Which of the following is false about assumptions and conditions for multiple linear regression?

A.) Residuals should have a linear relationship with the response variable.

B.) Explanatory variables should have linear relationship with the response variable.

C.) Explanatory variables should not have strong linear relationships with each other.

D.) Residuals should be normally distributed around 0.

Answer 1

A. FALSE . Because, any linear regression model is designed such a way that the residuals do not get to show any kind of pattern or, residuals should be random points around the regression lines. In other words, it is assumed that residuals follow a normal distribution.

B. TRUE. There must be at least an approximate linear relationship between the explanatory and response variables, otherwise the linear regression model won't work.

C. TRUE. One of the assumptions of Multiple linear regression is : absence of multicolinearity ie, correlation between the independent variables, which should not occur. If present, must be removed using some technique.

D. TRUE. Already explained in question A.