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This section includes 15 Mcqs, each offering curated multiple-choice questions to sharpen your Data Science knowledge and support exam preparation. Choose a topic below to get started.
1. |
The least squares estimate for the coefficient of a multivariate regression model is exactly regression through the origin with the linear relationships. |
A. | True |
B. | False |
Answer» C. | |
2. |
Which of the following show residuals divided by their standard deviations? |
A. | rstudent |
B. | cooks.distance |
C. | rstandard |
D. | all of the mentioned |
Answer» D. all of the mentioned | |
3. |
Residual ______ plots investigate normality of the errors. |
A. | RR |
B. | PP |
C. | |
D. | None of the mentioned |
Answer» D. None of the mentioned | |
4. |
Which of the following can be useful for diagnosing data entry errors? |
A. | hat values |
B. | dffit |
C. | resid |
D. | all of the mentioned |
Answer» B. dffit | |
5. |
Which of the following statement is incorrect with respect to outliers? |
A. | Outliers can have varying degrees of influence |
B. | Outliers can be the result of spurious or real processes |
C. | Outliers cannot conform to the regression relationship |
D. | None of the mentioned |
Answer» D. None of the mentioned | |
6. |
Which of the following things can be accomplished with linear model? |
A. | Flexibly fit complicated functions |
B. | Uncover complex multivariate relationships |
C. | Build accurate prediction models |
D. | All of the mentioned |
Answer» E. | |
7. |
Which of the following is the correct formula for total variation? |
A. | Total Variation = Residual Variation – Regression Variation |
B. | Total Variation = Residual Variation + Regression Variation |
C. | Total Variation = Residual Variation * Regression Variation |
D. | All of the mentioned |
Answer» C. Total Variation = Residual Variation * Regression Variation | |
8. |
WHICH_OF_THE_FOLLOWING_SHOW_RESIDUALS_DIVIDED_BY_THEIR_STANDARD_DEVIATIONS_??$ |
A. | rstudent |
B. | cooks.distance |
C. | rstandard |
D. | all of the Mentioned |
Answer» D. all of the Mentioned | |
9. |
The_least_squares_estimate_for_the_coefficient_of_a_multivariate_regression_model_is_exactly_regression_through_the_origin_with_the_linear_relationships.$ |
A. | True |
B. | False |
Answer» C. | |
10. |
Residual ______ plots investigate normality of the errors? |
A. | RR |
B. | PP |
C. | |
D. | None of the Mentioned |
Answer» D. None of the Mentioned | |
11. |
Multivariate regression estimates are exactly those having removed the linear relationship of the other variables from both the regressor and response. |
A. | True |
B. | False |
Answer» B. False | |
12. |
Which of the following can be useful for diagnosing data entry errors ? |
A. | hat values |
B. | dffit |
C. | resid |
D. | all of the Mentioned |
Answer» B. dffit | |
13. |
Which of the following statement is incorrect with respect to outliers ? |
A. | Outliers can have varying degrees of influence |
B. | Outliers can be the result of spurious or real processes. |
C. | Outliers cannot conform to the regression relationship |
D. | None of the Mentioned |
Answer» D. None of the Mentioned | |
14. |
Which of the following things can be accomplished with linear model ? |
A. | Flexibly fit complicated functions |
B. | Uncover complex multivariate relationships |
C. | Build accurate prediction models |
D. | All of the Mentioned |
Answer» E. | |
15. |
Which of the following is correct formula for total variation ? |
A. | Total Variation = Residual Variation – Regression Variation |
B. | Total Variation = Residual Variation + Regression Variation |
C. | Total Variation = Residual Variation * Regression Variation |
D. | All of the Mentioned |
Answer» C. Total Variation = Residual Variation * Regression Variation | |