Multiple regression analysis is based on several key assumptions:
Linearity: The relationship between the dependent and independent variables is linear. Independence: Observations are independent of each other. Homoscedasticity: The variance of the residuals is constant across all levels of the independent variables. No Perfect Multicollinearity: Independent variables are not perfectly correlated. Normality: The residuals (errors) are normally distributed.
