The second assumption relates to variance (Section 1.8.5) and is called homoscedasticity (also known as homogeneity of variance). It impacts two things: Parameters: Using the method of least squares (Section 2.6) to estimate the parameters in the model, we get optimal estimates if the variance of the outcome variable is equal across different values of the predictor variable. Null hypothesis significance testing: Test statistics often assume that the variance of the outcome variable is equal across different values of the predictor variable. If this is not the case then these test statistics
...more
