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
      specified.

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).