5 Assumptions of Multiple Linear Regression

1. Linearity: The relationship between the independent variables and the dependent variable is assumed to be linear. 2. No Multicollinearity: The independent variables should not be highly correlated with each other. 3. Normality of Error Terms: The error terms should be normally distributed. 4. Homoscedasticity: The variance of error terms should be constant for all values of the independent variables. 5. Independence of Error Terms: The error terms should be independent of each other.