Chapter 12: OLS assumptions and diagnostic testing
‘Regression Diagnostics’
Question 1: What are the key assumptions of the linear regression model identified in the video?
Answer 1: Linearity between mean response and explanatory variable, normality of errors, constant variance of errors and independence between observations.
‘Checking Linear Regression Assumptions in R’
Question 1: Will the OLS assumptions ever be perfectly met when using ‘real’ data?
Answer 1: No, but we want to assess whether our model does ‘well enough’ in satisfying the assumptions.
Question 2: What are the errors in a regression model?
Answer 2: They are the difference between the actual y value and the predicted y value for any given value of x.
Question 3: How is non-constant error variance (heteroscedasticity) checked in the video?
Answer 3: It is checked by examining a residuals vs. fitted values plot. And in the video, it appears that the residuals are non-constant and thus heteroscedasticity is present.
