QUESTION 13 In the MLR model, what do we mean by Heteroskedasticity? That the error term depends on the values of the explanatory variables That all the explanatory variables have different variance That the variance of the error term is a function of the explanatory variables That the variance of the error term is constant
The Simple Linear Regression model is
Y = b0 + b1*X1 + u
and the Multiple Linear Regression model with k variables is:
Y = b0 + b1*X1 + b2*X2 + ... + bk*Xk + u
Y is the dependent variable, the X1, X2, ..., Xk are the explanatory variables, b0 is the intercept, b1, b2, ..., bk are the slope coefficients, and u is the error term,
Yhat represents the OLS fitted values, uhat represent the OLS residuals, b0_hat represents the OLS estimated intercept, and b1_hat, b2_hat,..., bk_hat, represent the OLS estimated slope coefficients.
QUESTION 13
In the MLR model, what do we mean by Heteroskedasticity?
- That the error term depends on the values of the explanatory variables
- That all the explanatory variables have different variance
- That the variance of the error term is a
function of the explanatory variables - That the variance of the error term is constant
QUESTION 14
Suppose that in the model Y=b0+b1*X1+u, we add a variable that is
- It will go up
- It will go down
- We cannot say with the provided information
- It will remain unchanged
QUESTION 15
Suppose you have an MLR model that includes an intercept, with 150 observations and 11 variables. If assumptions MLR1-MLR6 hold, a t-statistic for any of the coefficients in this model follows the t-distribution with degrees of freedom equal to:
- 138
- 11
- 150
- 139
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