Question

What is the broad effect of CLRM without proper diagnostic

Answer 1

CLRM(Classical linear regression model) is required to show that the estimation technique, ordinary least squares (OLS), had a number of desirable properties, and also so that hypothesis tests regarding the coefficient estimates could validly be conducted. Specifically, it was assumed that:

Y_{i =} b₁ *x_{1i +} b₂*x_{2i +} b₃ *x_{3i +.......+}b_n*xni + u_i

u_{i= error term}

(1) mean =E(u_i) = 0 ( The errors have zero mean)

(2) variance=var(ut) = σ 2 < ∞( Variance will be constant over all values off x_i i.e homoscedasticity)

(3) Co-variance=cov(u_i ,u_j) = 0 ;( i) does not equal to (j) (The errors are statistically independent of one another i.e no autocorelation)

(4) Co-variance=cov(u_i ,x_j) = 0 ; ( No relationship between error terms(u_i) and corresponding variable (x_i)

(5) u_i = N(mean=0, variance ) ; (the error terms are normally distributed)