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).