The general answer to the question of choosing the scale of the variables is
A. dependent on you whim.
B. to make the regression results easy to read and to interpret.
C. to ensure that the regression coefficients always lie between -1 and 1.
D. irrelevant because regardless of the scale of the variable, the regression coefficient is unaffected.
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If the estimates of the coefficients of interest change substantially across specifications,
A. then this can be expected from sample variation.
B. then you should change the scale of the variables to make the changes appear to be smaller.
C. then this often provides evidence that the original specification had omitted variable bias.
D. then choose the specification for which your coefficient of interest is most significant.
Consider a regression with two variables, in which X1i is the variable of interest and X2i is the control variable. Conditional mean independence requires
A. E(ui|X1i, X2i) = E(ui|X2i)
B. E(ui|X1i, X2i) = E(ui|X1i)
C. E(ui|X1i) = E(ui|X2i)
D. E(ui) = E(ui|X2i)
If the explained sum of squares is 35 and the total sum of squares is 49, what is the residual sum of squares?
A. 10
B. 12
C. 18
D. 14
Which of the following is true of R2?
A. R2 is also called the standard error of regression.
B. A low R2 indicates that the Ordinary Least Squares line fits the data well.
C. R2 usually decreases with an increase in the number of independent variables in a regression.
D. R2 shows what percentage of the total variation in the dependent variable, Y, is explained by the explanatory variables.
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