The t-statistic is calculated by dividing
A. the OLS estimator by its standard error.
B. the slope by the standard deviation of the explanatory variable.
C. the estimator minus its hypothesized value by the standard error of the estimator.
D. the slope by 1.96.
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If the absolute value of your calculated t-statistic exceeds the critical value from the standard normal distribution, you can
A. reject the null hypothesis.
B. safely assume that your regression results are significant.
C. reject the assumption that the error terms are homoskedastic.
D. conclude that most of the actual values are very close to the regression line.
Under the least squares assumptions (zero conditional mean for the error term, Xi and Yi being i.i.d., and Xi and ui having finite fourth moments), the OLS estimator for the slope and intercept
A. has an exact normal distribution for n > 15.
B. is BLUE.
C. has a normal distribution even in small samples.
D. is unbiased.
The construction of the t-statistic for a one- and a two-sided hypothesis
A. depends on the critical value from the appropriate distribution.
B. is the same.
C. is different since the critical value must be 1.645 for the one-sided hypothesis, but 1.96 for the two-sided hypothesis (using a 5% probability for the Type I error).
D. uses ±1.96 for the two-sided test, but only +1.96 for the one-sided test.
The p-value for a one-sided left-tail test is given by
A. Pr(Z - tact ) = φ(tact).
B. Pr(Z < tact ) = φ(tact).
C. Pr(Z < tact ) < 1.645.
D. cannot be calculated, since probabilities must always be positive.
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