MCQs on Correlation and Regression 3

This Post contains MCQs on Correlation and Regression Analysis, Multiple Regression Analysis, Coefficient of Determination (Explained Variation), Unexplained Variation, Model Selection Criteria, Model Assumptions, Interpretation of results, Intercept, Slope, Partial Correlation, Significance tests, Multicollinearity, Heteroscedasticity, Autocorrelation, etc. Let us start MCQs on Correlation and Regression Analysis.

MCQs about Correlation and Regression Analysis

1. If $b_{yx} <0$ and $b_{xy} =<0$, then $r$ is

 
 
 
 

2. Two regression lines are parallel to each other if their slope is

 
 
 
 

3. The Coefficient of Correlation $r$ is independent of

 
 
 
 

4. If $r=0.6, b_{yx}=1.2$ then $b_{xy}=?$

 
 
 
 

5. In the regression line $Y=a+bX$ the following is always true

 
 
 
 

6. When two regression coefficients bear the same algebraic signs, then the correlation coefficient will be

 
 
 
 

7. It is possible that two regression coefficients have

 
 
 
 

8. The Coefficient of Correlation between $X$ and $X$ is

 
 
 
 

9. When $b_{xy}$ is positive, then $b_{yx}$ will be

 
 
 
 

10. If $r_{xy} = -0.84$ then $r_{yx}=?$

 
 
 
 

11. The coefficient of Correlation values lies between

 
 
 
 

12. A perfect negative correlation is signified by

 
 
 
 

13. In the regression line $Y=a+bX$

 
 
 
 

14. If $\hat{Y}=a$ then $r_{xy}$?

 
 
 
 

15. If $X$ and $Y$ are independent of each other, the Coefficient of Correlation is

 
 
 
 

16. When the regression line passes through the origin then

 
 
 
 

17. If two variables oppose each other then the correlation will be

 
 
 
 

18. In Correlation, both variables are always

 
 
 
 

19. The regression coefficient is independent of

 
 
 
 

20. The Coefficient of Correlation between $U=X$ and $V=-X$ is

 
 
 
 


Correlation is a statistical measure used to determine the strength and direction of the mutual relationship between two quantitative variables. The correlation measures the interdependence between two continuous variables. The regression describes how an explanatory variable is numerically related to the dependent variables.

Both of the tools are used to represent the linear relationship between the two quantitative variables. The relationship between variables can be observed either using a graphical representation between the variables or numerical computation using an appropriate computational formula.

MCQs on Correlation and Regression Analysis with Answers

Online MCQs on Correlation and Regression Analysis

  • The coefficient of Correlation values lies between
  • If $r_{xy} = -0.84$ then $r_{yx}=?$
  • In Correlation, both variables are always
  • If two variables oppose each other then the correlation will be
  • A perfect negative correlation is signified by
  • The Coefficient of Correlation between $U=X$ and $V=-X$ is
  • The Coefficient of Correlation between $X$ and $X$ is
  • The Coefficient of Correlation $r$ is independent of
  • If $X$ and $Y$ are independent of each other, the Coefficient of Correlation is
  • If $b_{yx} <0$ and $b_{xy} =<0$, then $r$ is
  • If $r=0.6, b_{yx}=1.2$ then $b_{xy}=?$
  • When the regression line passes through the origin then
  • Two regression lines are parallel to each other if their slope is
  • When $b_{xy}$ is positive, then $b_{yx}$ will be
  • If $\hat{Y}=a$ then $r_{xy}$?
  • When two regression coefficients bear the same algebraic signs, then the correlation coefficient will be
  • It is possible that two regression coefficients have
  • The regression coefficient is independent of
  • In the regression line $Y=a+bX$
  • In the regression line $Y=a+bX$ the following is always true
MCQs on Correlation and Regression analysis

Note that neither regression nor correlation analyses can be interpreted as establishing some cause-and-effect relationships. Both of these can be used to indicate only how or to what extent the variables under study are associated (or mutually related) with each other. The correlation coefficient measures only the degree (strength) and direction of linear association between the two variables. Any conclusions about a cause-and-effect relationship must be based on the judgment of the analyst.

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