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13_Estimating Correlation and Regression-1

# 13_Estimating Correlation and Regression-1 - R Square...

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Eric Schneck 1. Use the data posted on the course website to do the following: a. Create a scatter plot, where the return on the S&P 500 is the x -variable, and the return on IBM is the y variable. b. Estimate a and b in the equation E[r IBM | r S&P ]=a+br S&P . Use the excel functions covar and varp . A= .004518 B= .989162 c. Now estimate a and b in the equation E[r IBM | r S&P ]=a+br S&P using excel’s regression package. Test the null hypothesis that the true a is 0. Test the null hypothesis that the true b is 1. SUMMARY OUTPUT Regression Statistics Multiple R 0.603133 483 R Square

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Unformatted text preview: R Square 0.363769 998 Adjusted R Square 0.362706 068 Standard Error 0.056544 413 Observati ons 600 ANOVA df SS MS F Significanc e F Regressio n 1 1.093184 141 1.093184 141 341.9116 639 1.02889E-60 Residual 598 1.911967 872 0.003197 271 Total 599 3.005152 013 Coefficient s Standard Error t Stat P-value Lower 95% Upper 95% Intercept 0. 0045176 38 0.002329 366 1.939427 766 0.052919 222-5.70952E-05 0.009092 37 X Variable 1 0. 9891616 51 0.053494 645 18.49085 352 1.02889E-60 0.884101 441 1.094221 861...
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13_Estimating Correlation and Regression-1 - R Square...

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