A linear probability model is likely to violate which assumption of MR most of the time? A)The values of each x_ik are not random and are not exact linear functions of the other explanatory variables B)var(y_i. )= var(e_i)= $\sigma$² C)E(y_i)= $\beta$₁ + $\beta$₂x_i2 + $\beta$₃x_i3 + …….+ $\beta$_kx_ik,⟺E(e_i)= 0 D)cov(y_i,y_j)= cov(e_i,e_j)= 0; (i≠j)

Question

Quizplus · Accepted Answer

The linear probability model often violates the assumption of homoscedasticity (constant variance of error terms), which is what choice B refers to. In a linear probability model, the variance of the error term can depend on the values of the independent variables, leading to heteroscedasticity.

A Linear Probability Model Is Likely to Violate Which Assumption σ\sigmaσ

A Linear Probability Model Is Likely to Violate Which Assumption $\sigma$