Explore topic-wise MCQs in Data Science.

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. QQ
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. QQ
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