Advanced MCQ on Regression with Answer Key (Download PDF)

Regression is a statistical method used to analyse the relationship between two or more variables. It helps determine how the dependent variable changes when one or more independent variables are modified. The most common form is linear regression, where the relationship is modelled with a straight line (y = mx + c), where “y” is the dependent variable, “x” is the independent variable, “m” is the slope, and “c” is the intercept. Regression is widely used for prediction, trend analysis, and determining cause-effect relationships in various fields like economics, biology, and machine learning. This is an advanced MCQ on Regression with Answer Key. You can download the PDF of this MCQ from the link provided in the result windows of the quiz.

Biostatistics Notes  |  Biostatistics PPTs  |  Biostatistics MCQs

1. 
In multiple linear regression, the assumption of multicollinearity refers to:

2. 
Which of the following is NOT an assumption of linear regression?

3. 
What is the primary purpose of using a residual plot in regression analysis?

4. 
In a multiple regression analysis, if one of the predictors is perfectly collinear with another predictor, what happens?

5. 
Which of the following measures the strength and direction of the linear relationship between two variables?

6. 
In logistic regression, the dependent variable is:

7. 
The least squares method in regression analysis aims to minimize:

8. 
Which method is typically used to estimate the parameters in a linear regression model?

9. 
In a simple linear regression, if the correlation coefficient between X and Y is -0.8, what can be said about the slope of the regression line?

10. 
In multiple regression, the F-test is used to determine:

11. 
Which of the following best describes the Durbin-Watson statistic in regression analysis?

12. 
In ridge regression, what is the primary purpose of adding a penalty term to the regression equation?

13. 
Which of the following is true about the LASSO (Least Absolute Shrinkage and Selection Operator) method in regression?

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