Chapter 9: Correlation and Regression AnalysisMarch 13, 2024 Maven Leave a Comment Welcome to the Chapter 9: Correlation and Regression Analysis Quiz! Name Email 1. Correlation co-efficient lies between 0 to $\infty$ –1 to +1 –1 to 0 -1 to $\infty$ None 2. The variable whose value is influenced (or) is to be predicted is called dependent variable independent variable regressor explanatory variable None 3. From the following data N=11, $\sum X=117$, $\sum Y=260$, $\sum X^{2}=1313$, $\sum Y^{2}=6580$, $\sum XY=2827$, the correlation coefficient is 0.3566 –0.3566 0 0.4566 None 4. The correlation coefficient from the following data N=25, $\sum X=125$, $\sum Y=100$, $\sum X^{2}=650$, $\sum Y^{2}=436$, $\sum XY=520$ 0.667 -0.006 –0.667 0.70 None 5. If r(X,Y) = 0 the variables X and Y are said to be Positive correlation Negative correlation No correlation Perfect positive correlation None 6. Example for positive correlation is Income and expenditure Price and demand Repayment period and EMI Weight and Income None 7. The correlation coefficient is r(X,Y)=$\frac{\sigma x \sigma y}{cov(x,y)}$ r(X,Y)=$\frac{cov(x,y)}{\sigma x \sigma y}$ r(X,Y)=$\frac{cov(x,y)}{\sigma y}$ r(X,Y)=$\frac{cov(x,y)}{\sigma x}$ None 8. If the values of two variables move in opposite direction then the correlation is said to be Negative Positive Perfect positive No correlation None 9. If the values of two variables move in same direction then the correlation is said to be Negative Positive Perfect positive No correlation None 10. The variable which influences the values or is used for prediction is called Dependent variable Independent variable Explained variable Regressed None 11. The regression coefficient of X on Y $b_{xy}=\frac{N\sum dxdy-(\sum dx)(\sum dy)}{N\sum dy^{2}-(\sum (dy)^{2})}$ $b_{yx}=\frac{N\sum dxdy-(\sum dx)(\sum dy)}{N\sum dy^{2}-(\sum (dy)^{2})}$ $b_{xy}=\frac{N\sum dxdy-(\sum dx)(\sum dy)}{N\sum dx^{2}-(\sum (dx)^{2})}$ $b_{y}=\frac{N\sum xy-(\sum x)(\sum y)}{\sqrt {N\sum x^{2}-(\sum (x)^{2})}\times \sqrt{N\sum y^{2}-(\sum (y)^{2}}}$ None 12. If X and Y are two variates, there can be at most One regression line two regression lines three regression lines more regression lines None 13. If regression co-efficient of Y on X is 2, then the regression co-efficient of X on Y is $\leq \frac{1}{2}$ 2 $> \frac{1}{2}$ 1 None 14. Scatter diagram of the variate values (X,Y) give the idea about functional relationship regression model distribution of errors no relation None 15. The correlation coefficient r=$\pm \sqrt{b_{xy}\times b_{yx} }$ r=$\frac{1}{\sqrt{b_{xy}\times b_{yx} }}$ r=${b_{xy}\times b_{yx} }$ r=$\pm \sqrt{\frac{1}{{b_{xy}\times b_{yx} }}}$ None 16. When one regression coefficient is negative, the other would be Negative Positive Zero None of them None 17. The person suggested a mathematical method for measuring the magnitude of linear relationship between two variables say X and Y is Karl Pearson Spearman Croxton and Cowden Ya Lun Chou None 18. The lines of regression of X on Y estimates X for a given value of Y Y for a given value of X X from Y and Y from X none of these None 19. The regression coefficient of Y on X $b_{xy}=\frac{N\sum dxdy-(\sum dx)(\sum dy)}{N\sum dy^{2}-\sum (dy)^{2}}$ $b_{yx}=\frac{N\sum dxdy-(\sum dx)(\sum dy)}{N\sum dy^{2}-\sum (dy)^{2}}$ $b_{xy}=\frac{N\sum dxdy-(\sum dx)(\sum dy)}{N\sum dx^{2}-\sum (dx)^{2}}$ $b_{y}=\frac{N\sum xy-(\sum x)(\sum y)}{\sqrt {N\sum x^{2}-(\sum (x)^{2})}\times \sqrt{N\sum y^{2}-\sum (y)^{2}}}$ None 20. If two variables moves in decreasing direction then the correlation is positive negative perfect negative no correlation None 21. The lines of regression intersect at the point (X,Y) $(\bar{X}, \bar{Y})$ (0,0) $(\sigma _{x},\sigma _{y})$ None 22. If Cov(x,y)=–16.5, $\sigma _{x}^{2}=2.89$ $\sigma _{y}^{2}=100$, Find correlation coefficient. –0.12 0.001 –1 –0.97 None 23. If r=–1 , then correlation between the variables perfect positive perfect negative negative no correlation None 24. The term regression was introduced by R.A. Fisher Sir Francis Galton Karl Pearson Croxton and Cowden None 25. The coefficient of correlation describes the magnitude and direction only magnitude only direction no magnitude and no direction None Time's upRelated Posts:Chapter 4: Cost and Revenue AnalysisChapter 9: Ratio AnalysisChapter 8: Financial Statement AnalysisChapter 6: Distribution Analysis
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