Assumptions of the Classical Linear Regression Model


1.  The dependent variable is linearly related to the coefficients of the model and the model is correctly

2.  The independent variable(s) is/are uncorrelated with the equation error term.

3.  The mean of the error term is zero.

4.  The error term has a constant variance (homoscedastic error).  No heteroscedasticity.

5.  The error terms are uncorrelated with each other.  No autocorrelation or serial correlation.

6.  No perfect multicollinearity.  No independent variable has a perfect linear relationship with any of the
     other independent variables.

7.  The error term is normally distributed (optional assumption for hypothesis testing).