In the classical linear regression model, X_i and U_i are assumed to be:

The correct answer is not correlated

Key Points Xi = explanatory variable/ Independent Variable

U i = error term 

Important Points Assumptions of the Classical Linear Regression Model:

• The regression model is linear, correctly specified, and has an additive error term.
• The error term has a zero population mean.
• All explanatory variables are uncorrelated with the error term.
• Observations of the error term are uncorrelated with each other (no serial correlation).
• The error term has a constant variance (no heteroskedasticity).
• No explanatory variable is a perfect linear function of any other explanatory variables (no perfect multicollinearity).
• The error term is normally distributed (not required).