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