The formula for the standard error of the regression coefficient, when moving from one explanatory variable to two explanatory variables,
A. stays the same.
B. changes, unless the second explanatory variable is a binary variable.
C. changes.
D. changes, unless you test for a null hypothesis that the addition regression coefficient is zero.
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When your multiple regression function includes a single omitted variable regressor, then
A. use a two-sided alternative hypothesis to check the influence of all included variables.
B. the estimator for your included regressors will be biased if at least one of the included variables is correlated with the omitted variable.
C. the estimator for your included regressors will always be biased.
D. lower the critical value to 1.645 from 1.96 in a two-sided alternative hypothesis to test the significance of the coefficients of the included variables.
The OLS estimators of the coefficients in multiple regression will have omitted variable bias
A. only if an omitted determinant of Yi is a continuous variable.
B. if an omitted variable is correlated with at least one of the regressors, even though it is not a determinant of the dependent variable.
C. only if the omitted variable is not normally distributed.
D. if an omitted determinant of Yi is correlated with at least one of the regressors.
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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