Chapter 9: Correlation and Regression Analysis March 13, 2024 Maven Leave a Comment Welcome to the Chapter 9: Correlation and Regression Analysis Name Email 1. 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 2. When one regression coefficient is negative, the other would be Negative Positive Zero None of them None 3. 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 4. If two variables moves in decreasing direction then the correlation is positive negative perfect negative no correlation None 5. 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 6. 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 7. 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 8. Scatter diagram of the variate values (X,Y) give the idea about functional relationship regression model distribution of errors no relation None 9. 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 10. 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 11. The term regression was introduced by R.A. Fisher Sir Francis Galton Karl Pearson Croxton and Cowden None 12. If r=–1 , then correlation between the variables perfect positive perfect negative negative no correlation None 13. 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 14. The lines of regression intersect at the point (X,Y) $(\bar{X}, \bar{Y})$ (0,0) $(\sigma _{x},\sigma _{y})$ None 15. The coefficient of correlation describes the magnitude and direction only magnitude only direction no magnitude and no direction None 16. 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 17. Correlation co-efficient lies between 0 to $\infty$ –1 to +1 –1 to 0 -1 to $\infty$ None 18. 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 19. If r(X,Y) = 0 the variables X and Y are said to be Positive correlation Negative correlation No correlation Perfect positive correlation None 20. 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 21. The variable whose value is influenced (or) is to be predicted is called dependent variable independent variable regressor explanatory variable None 22. Example for positive correlation is Income and expenditure Price and demand Repayment period and EMI Weight and Income None 23. If the values of two variables move in opposite direction then the correlation is said to be Negative Positive Perfect positive No correlation None 24. The variable which influences the values or is used for prediction is called Dependent variable Independent variable Explained variable Regressed None 25. If the values of two variables move in same direction then the correlation is said to be Negative Positive Perfect positive No correlation None Time's up Related Posts:Chapter 4: Cost and Revenue AnalysisChapter 2. Consumption AnalysisChapter 3: Production AnalysisChapter 6: Distribution Analysis
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