Regression Statistics Multiple R 0.451216205 R Square 0.203596063 Adjusted R Square 0.190097692 Standard Error 0.051791629 Observations 61 ANOVA df SS MS F Significance F Regression 1 0.040458253 0.040458253 15.083009 0.000262577 Residual 59 0.158259997 0.002682373 Total 60 0.19871825 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 0.00987396 0.006785133 1.455234544 0.150904641 -0.00370306 0.023450979 -0.00370306 0.023450979 S&P 0.752212332 0.193685208 3.883684976 0.000262577 0.364649126 1.139775537 0.364649126 1.139775537 Current estimate given to us...

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Question

Basic Math

Regression Statistics
Multiple R 0.451216205
R Square 0.203596063
Adjusted RSquare 0.190097692
StandardError 0.051791629
Observations 61
ANOVA
df SS MS F Significance F
Regression 1 0.040458253 0.040458253 15.083009 0.000262577
Residual 59 0.158259997 0.002682373
Total 60 0.19871825
Coefficients Standard Error tStat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 0.00987396 0.006785133 1.455234544 0.150904641 -0.00370306 0.023450979 -0.00370306 0.023450979
S&P 0.752212332 0.193685208 3.883684976 0.000262577 0.364649126 1.139775537 0.364649126 1.139775537
Current estimate given to us in the directions
1.07
RESIDUALOUTPUT
Observation Predicted Y Residuals Standard Residuals
1 0.038198737 -0.01978845 -0.385302506
2 -0.00179574 0.144257104 2.808841664
1. How does your estimate of beta compare with the beta estimateprovided (1.07)? Why might your estimate differ from estimated betaof 1.07?
2. How much of the variability of your security’s return is“explained” by the variability of returns in the “market”? (Note:In your case, the market is represented by the S&P 500 Index.)Do you think that a different market index might be a betterrepresentation of the market for your particular security? Why/Whynot?
3. What is the correlation of returns for your security with themarket for the selected time period? Might this relationship changeover time, and if so, how and why?
4. Does the relationship between your security and the marketappear to be statistically significantly different than zero? Whatevidence from the regression supports your conclusion?
5. Review the standardized residuals and comment about theimportance of individual data points (if any) that may haveinfluenced your estimation of beta. (observation 2 is the onlyskewed one)

Answer & Explanation Solved by verified expert

3.7 Ratings (289 Votes)

1 I think the estimate of beta provided here is instructed maybe it is a regression outcome from a different regression model The current model has an output of beta which is in the same direction but as it is a linear regression model so it will have intercept and slope So beta will determine the slope of the straight line As the beta obtained from the outcome is smaller than the beta mentioned here in the question hence we can say that the straight line will be slightly less steep than the mentioned regression line because the slope is smaller than the See Answer

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Regression Statistics
Multiple R	0.451216205
R Square	0.203596063
Adjusted RSquare	0.190097692
StandardError	0.051791629
Observations	61
ANOVA
	df	SS	MS	F	Significance F
Regression	1	0.040458253	0.040458253	15.083009	0.000262577
Residual	59	0.158259997	0.002682373
Total	60	0.19871825
	Coefficients	Standard Error	tStat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
Intercept	0.00987396	0.006785133	1.455234544	0.150904641	-0.00370306	0.023450979	-0.00370306	0.023450979
S&P	0.752212332	0.193685208	3.883684976	0.000262577	0.364649126	1.139775537	0.364649126	1.139775537
Current estimate given to us in the directions
	1.07
RESIDUALOUTPUT
Observation	Predicted Y	Residuals	Standard Residuals
1	0.038198737	-0.01978845	-0.385302506
2	-0.00179574	0.144257104	2.808841664