Regression Assumptions | Linear Regression Model Assumptions Part 2

This video explains the assumptions of linear regression model. These assumptions must be fulfilled before the output of a regression model can be reliably interpreted. Following assumptions are explained in this video: Linear Regression Model Normality of error term Homoscedasticity No Autocorrelation N must be greater than number of parameters Non-constant X No Multicollinearity Exogeneity