# Tagged: MCQs Regression

## Correlation and Regression 4

This quiz is about MCQs Regression and Correlation analysis.

This Quiz contains MCQs about 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, OLS Assumptions, Multicollinearity, Heteroscedasticity, Autocorrelation, graphical representation of the relationship between the variables, etc. Let us start MCQs about Correlation and Regression Analysis.

1. If the scatter diagram is drawn the scatter points lie on a straight line then it indicates

2. The predicted rate of response of the dependent variable to changes in the independent variable is called

3. If the regression equation is equal to $Y=23.6 – 54.2X$, then $23.6$ is the ______ while $-54.2$ is the ____ of the regression line.

4. The regression equation is the line with slope passing through

5. Which of the following can never be taken as coefficient of correlation?

6. When $\beta_{yx}$ is positive, then $\beta_{xy}$ will be

8. The slope of the regression line of $Y$ on $X$ is also called

9. The value of the coefficient of correlation lies between

10. In regression equation $y=\alpha + \beta X + e$, both $X$ and $y$ variables are

11. If the equation of the regression line is $y = 5$, then what result will you take out from it?

12. If $r=0.6$ and $\beta_{yx}=1.8$ then $\beta_{xy} = ?$

13. Which of the following relationship holds

14. In the model $Y= mX+ a\,\,\,$, $Y$ is also known as the:

15. If $R^2$ is zero, that is no collinearity/ Multicollinearity, the variance inflation factor (VIF) will be

16. If $Y=2+0.6x$ then the value of slope will be

17. The method of least squares finds the best fit line that _______ the error between observed & estimated points on the line

18. In simple regression the number of unknown constants are

19. In simple regression equation, the number of variables are

20. If $Y=2+0.6X$ then the value of $Y$-intercept will be

Correlation is a statistical measure used to determine the strength and direction of the mutual relationship between two quantitative variables. The value of the correlation lies between $-1$ and $1$. 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 graphical representation between the variables or numerical computation using appropriate computational formula. 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.

## Correlation and Regression 2

This Quiz contains MCQs about 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, OLS Assumptions, Multicollinearity, Heteroscedasticity, Autocorrelation, graphical representation of the relationship between the variables, etc. Let us start MCQs about Correlation and Regression Analysis.

Please go to Correlation and Regression 2 to view the test

Correlation is a statistical measure used to determine the strength and direction of the mutual relationship between two quantitative variables. The value of correlation lies between $-1$ and $1$. 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 graphical representation between the variables or numerical computation using appropriate computational formula.

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.

## Correlation and Regression 1

This quiz is about MCQ on correlation and regression analysis.

This Section contains MCQs on Correlation Analysis, Simple 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, OLS Assumptions, Multicollinearity, Heteroscedasticity, Autocorrelation, etc. Let us start MCQ on Correlation and Regression Analysis

Please go to Correlation and Regression 1 to view the test

Correlation is a statistical measure used to determine the strength and direction of the mutual relationship between two quantitative 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 graphical representation between the variables or numerical computation using appropriate computational formula.

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.