1. |
## Two events, A and B, are said to be mutually exclusive if: |

A. | P(A | B) = 1 |

B. | P(B | A) = 1 |

C. | P(A and B) = 1 |

D. | P(A and B) = 0 |

Answer» D. P(A and B) = 0 |

2. |
## A Type I error occurs when we: |

A. | reject a false null hypothesis |

B. | reject a true null hypothesis |

C. | do not reject a false null hypothesis |

D. | do not reject a true null hypothesis |

Answer» B. reject a true null hypothesis |

3. |
## What is the meaning of the term "heteroscedasticity"? |

A. | The variance of the errors is not constant |

B. | The variance of the dependent variable is not constant |

C. | The errors are not linearly independent of one another |

D. | The errors have non- zero mean |

Answer» A. The variance of the errors is not constant |

4. |
## What would be then consequences for the OLS estimator if heteroscedasticity is present in a regression model but ignored? |

A. | It will be ignored |

B. | It will be inconsistent |

C. | It will be inefficient |

D. | All of a),c), b) will be true. |

Answer» C. It will be inefficient |

5. |
## Which one of the following is NOT a plausible remedy for near multicollinearity? |

A. | Use principal components analysis |

B. | Drop one of the collinear variables |

C. | Use a longer run of data |

D. | Take logarithems of each of the variables |

Answer» D. Take logarithems of each of the variables |

6. |
## What will be the properties of the OLS estimator in the presence of multicollinearity? |

A. | It will be consistent unbiased and efficient |

B. | It will be consistent and unbiased but not efficient |

C. | It will be consistent but not unbiased |

D. | It will not be consistent |

Answer» A. It will be consistent unbiased and efficient |

7. |
## A sure way of removing multicollinearity from the model is to |

A. | Work with panel data |

B. | Drop variables that cause multicollinearity in the first place |

C. | Transform the variables by first differencing them |

D. | Obtaining additional sample data |

Answer» B. Drop variables that cause multicollinearity in the first place |

8. |
## Autocorrelation is generally occurred in |

A. | Cross-section data |

B. | Time series data |

C. | Pooled data |

D. | None of the above |

Answer» B. Time series data |

9. |
## The regression coefficient estimated in the presence of autocorrelation in the sample data are NOT |

A. | Unbiased estimators |

B. | Consistent estimators |

C. | Efficient estimators |

D. | Linear estimators |

Answer» C. Efficient estimators |

10. |
## In the regression function y=α + βx +c |

A. | x is the regressor |

B. | Y is the regressor |

C. | x is the regressand |

D. | none of these |

Answer» A. x is the regressor |

11. |
## The coefficient of determination, r2 shows |

A. | Proportion of the variation in the dependent variable Y is explained by the independent variable X |

B. | Proportion of the variation in the dependent variable X is explained by the independent variable Y |

C. | Proportion of the variation in ui is explained by the independent variable X |

D. | Both a and c |

Answer» A. Proportion of the variation in the dependent variable Y is explained by the independent variable X |

12. |
## BLUE is |

A. | Best Linear Unbiased Estimator |

B. | Best Linear Unconditional Estimator |

C. | Basic Linear Unconditional Estimator |

D. | Both b and c |

Answer» A. Best Linear Unbiased Estimator |

13. |
## Data on one or variables collected at a given point of time |

A. | Panel Data |

B. | Time series data |

C. | Pooled data |

D. | Cross-section data |

Answer» D. Cross-section data |

14. |
## The violation of the assumption of constant variance of the residual is known as |

A. | Heteroscedasticity |

B. | Multicollinearity |

C. | Homoscedasticity |

D. | Autocorrelation |

Answer» A. Heteroscedasticity |

15. |
## Formula of coefficient determination is |

A. | 1+RSS/TSS |

B. | 1-RSS/ESS |

C. | 1-RSS/TSS |

D. | 1*RSS/TSS |

Answer» C. 1-RSS/TSS |

16. |
## In confidence interval estimation, α = 5%, this means that this interval includes the true β with probability of |

A. | 0.50% |

B. | 50% |

C. | 5% |

D. | 95% |

Answer» D. 95% |

17. |
## Consider a large population with a mean of 160 and a standard deviation of 25. A random sample of size 64 is taken from this population. What is the standard deviation of the sample mean? |

A. | 3.125 |

B. | 2.5 |

C. | 3.75 |

D. | 5.625 |

Answer» A. 3.125 |

18. |
## In the case of multicollinearity which test will be insignificant? |

A. | f test |

B. | t test |

C. | both a and b |

D. | both are significant |

Answer» B. t test |

19. |
## Hetroscedasticity is generally occurred in |

A. | Cross-section data |

B. | Time series data |

C. | Pooled data |

D. | None of the above |

Answer» A. Cross-section data |

20. |
## When there are both qualitative and quantitative variables are there in the model, |

A. | ANOVA |

B. | ANCOVA |

C. | CHI SQUARE |

D. | All of the above |

Answer» B. ANCOVA |

21. |
## When is the problem of dummy variable trap occur? |

A. | When we take dummy variables more than the categories |

B. | When we take dummy variables less than the categories |

C. | When we take dummy variables equal to the no of categories |

D. | Both a and c |

Answer» D. Both a and c |

22. |
## Durbin Watson test is associated with: |

A. | Heteroscedasticity |

B. | Multicollinearity |

C. | Autocorrelation |

D. | Both a and c |

Answer» C. Autocorrelation |

23. |
## All are the types of specification errors EXCEPT: |

A. | Omission of relevant variable |

B. | Inclusion of unnecessary variable |

C. | errors of measurement |

D. | over identified |

Answer» D. over identified |

24. |
## White's test is used for the detection of ---------- -? |

A. | multicollinearity |

B. | hetroscedasticity |

C. | Autocorrelation |

D. | None of the above |

Answer» B. hetroscedasticity |

25. |
## Which one is not the assumption of OLS? |

A. | Perfect Multicollinearity |

B. | zero covariance between error terms |

C. | equal variance of disturbances |

D. | Mean value of disturbances is |

Answer» A. Perfect Multicollinearity |

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