The OLS residuals in the multiple regression model
A. cannot be calculated because there is more than one explanatory variable.
B. can be calculated by subtracting the fitted values from the actual values.
C. are zero because the predicted values are another name for forecasted values.
D. are typically the same as the population regression function errors.
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The sample regression line estimated by OLS
A. has an intercept that is equal to zero.
B. is the same as the population regression line.
C. cannot have negative and positive slopes.
D. is the line that minimizes the sum of squared prediction mistakes.
The main advantage of using multiple regression analysis over differences in means testing is that the regression technique
A. allows you to calculate p-values for the significance of your results.
B. provides you with a measure of your goodness of fit.
C. gives you quantitative estimates of a unit change in X.
D. assumes that the error terms are generated from a normal distribution.
In a multiple regression framework, the slope coefficient on the regressor X2i
A. takes into account the scale of the error term.
B. is measured in the units of Yi divided by units of X2i.
C. is usually positive.
D. is larger than the coefficient on X1i.
Consider the multiple regression model with two regressors X1 and X2, where both variables are determinants of the dependent variable. You first regress Y on X1 only and find no relationship. However when regressing Y on X1 and X2, the slope coefficient of X1changes by a large amount. This suggests that your first regression suffers from
A. heteroskedasticity
B. perfect multicollinearity
C. omitted variable bias
D. dummy variable trap
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