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 14 Suppose that in the model Y=b0+b1*X1+u, we add a variable that is correlated with both Y and X1. What will happen to the standard error of the OLS estimator for b1? 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
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 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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