Homoskedasticity means that A)var(u_i|X_i)= $\sigma _ { \mathrm { ui } } ^ { 2 }$ B)var(X_i)= $\sigma _ { \mathrm { u } } ^ { 2 }$ C)var(u_i|X_i)= $\sigma _ { \mathrm { u } } ^ { 2 }$ D)var( $\hat {\mathrm { u }}$ _i|X_i)= $\sigma _ { \mathrm { ui } } ^ { 2 }$

Question

Quizplus · Accepted Answer

Homoskedasticity refers to the assumption that the variance of the error terms in a regression model is constant across all levels of the independent variable. Choice C correctly defines homoskedasticity as the variance of the error term $u$ given the independent variables $X$ being constant ($\sigma _ { \mathrm { u } } ^ { 2 }$).

Homoskedasticity Means That A)var(ui|Xi)= σui2\sigma _ { \mathrm { ui } } ^ { 2 }σui2​

Homoskedasticity Means That
A)var(u_i|X_i)= $\sigma _ { \mathrm { ui } } ^ { 2 }$