Câu hỏi: Which one of the following is NOT an assumption of the classical linear regression model?

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30/08/2021
3.2 10 Đánh giá

A. The explanatory variables are uncorrelated with the error terms

B. The disturbance terms have zero mean

C. The dependent variable is not correlated with the disturbance terms

D. The disturbance terms are independent of one another

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Câu hỏi khác cùng đề thi
Câu 1: Consider a standard normally distributed variable, a t-distributed variable with d degrees of freedom, and an F-distributed variable with (1, d) degrees of freedom. Which of the following statements is FALSE?

A. The standard normal is a special case of the t-distribution, the square of which is a special case of the F-distribution

B. Since the three distributions are related, the 5% critical values from each will be the same

C. Asymptotically, a given test conducted using any of the three distributions will lead to the same conclusion

D. The normal and t- distributions are symmetric about zero while the F- takes only positive values

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Câu 2: 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

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Câu 3: Which of the following is NOT correct with regard to the p-value attached to a test statistic?

A. p-values can only be used for two-sided tests

B. It is the marginal significance level where we would be indifferent between rejecting and not rejecting the null hypothesis

C. It is the exact significance level for the test

D. Given the p-value, we can make inferences without referring to statistical tables

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Câu 4: Which of the following would you expect to be a problem associated with adding lagged values of the dependent variable into a regression equation?

A. The assumption that the regressors are non-stochastic is violated

B. A model with many lags may lead to residual non-normality

C. Adding lags may induce multicollinearity with current values of variables

D. The standard errors of the coefficients will fall as a result of adding more explanatory variables

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Câu 5: 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

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Câu 6: Which of the following statements is correct concerning the conditions required for OLS to be a usable estimation technique?

A. The model must be linear in the parameters

B. The model must be linear in the variables

C. The model must be linear in the variables and the parameters

D. The model must be linear in the residuals

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