Reviewing model assumptions

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 ...

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