A linear regression model is only as good as the validity of its assumptions, which can be summarized as follows:
- Linearity: This is a linear relationship between the predictor and the response variables. If this relationship is not explicitly present, transformations (log, polynomial, exponent, and so on) of X or Y may solve the problem.
- Non-correlation of errors: This is a common problem in time series and panel data where
; if the errors are correlated, you run the risk of creating a poorly specified model. - Homoscedasticity: This refers to normally distributed and constant variance of errors, which means that ...
