Performing regression analysis requires certain assumptions to be met:
Linearity: The relationship between the independent and dependent variables should be linear. Independence: Observations should be independent of each other. Homoscedasticity: The variance of the residuals should be constant across all levels of the independent variables. Normality: The residuals should be approximately normally distributed. No Multicollinearity: Independent variables should not be highly correlated with each other.
