CLRM ( Classical Linear Regression Model) A1:E(ut)=0 A2:Var(ut)= δ^2 A3: cov(ui,uj)=0 for i is not equal to j A4: cov(xi,ui)=0 A5: ut~N(0, δ^2 ). But, what are the differences between the assumptions of CLRM and general linear model? and if cov(ui,uj) is not equal to 0, what willhappen?