Câu hỏi: Which of the following is the most accurate definition of the term “the OLS estimator”?

A. It comprises the numerical values obtained from OLS estimation

B. It is a formula that, when applied to the data, will yield the parameter estimates

C. It is equivalent to the term “the OLS estimate”

D. It is a collection of all of the data used to estimate a linear regression model.

Câu 1: The type I error associated with testing a hypothesis is equal to:

A. One minus the type II error

B. The confidence level

C. The size of the test

D. The size of the sample

Câu 2: Which of the following is NOT a good reason for including a disturbance term in a regression equation?

A. It captures omitted determinants of the dependent variable

B. To allow for the non-zero mean of the dependent variable

C. To allow for errors in the measurement of the dependent variable

D. To allow for random influences on the dependent variable

Câu 3: Which of the following is a correct interpretation of a “95% confidence interval” for a regression parameter?

A. We are 95% sure that the interval contains the true value of the parameter

B. We are 95% sure that our estimate of the coefficient is correct

C. We are 95% sure that the interval contains our estimate of the coefficient

D. In repeated samples, we would derive the same estimate for the coefficient 95% of the time

Câu 4: The numerical score assigned to the credit rating of a bond is best described as what type of number?

A. Continuous

B. Cardinal

C. Ordinal

D. Nominal

Câu 5: What is the relationship, if any, between t-distributed and F-distributed random variables?

A. A t-variate with z degrees of freedom is also an F(1, z)

B. The square of a t-variate with z degrees of freedom is also an F(1, z)

C. A t-variate with z degrees of freedom is also an F(z, 1)

D. There is no relationship between the two distributions

Câu 6: Consider an increase in the size of the test used to examine a hypothesis from 5% to 10%. Which one of the following would be an implication?

A. The probability of a Type I error is increased

B. The probability of a Type II error is increased

C. The rejection criterion has become more strict

D. The null hypothesis will be rejected less often