**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).**