1. Linearity: The relationship between the independent and dependent variables is linear.
2. Independence: The residuals (errors) are independent of each other.
3. Homoscedasticity: The variance of the residuals is constant across all levels of the independent variables.
4. Normality: The residuals are normally distributed.
5. No multicollinearity: The independent variables are not highly correlated with each other.
6. No autocorrelation: The residuals are not correlated with each other over time.
7. No omitted variable bias: All relevant variables are included in the model.
Assumptions of the multiple regression model
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