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Source Model .007361756 .02417575 Residual Total 8_KO 8 SP500 _cons SS .031537506 df 1 .007361756 249 .000097091 250 MS Coef. Std. Err. .4753727 .0545926 .0006781 .0006219 .00012615 Number of obs F(1, 249) Prob > F R-squared Adj R-squared Root MSE t P>|t| 8.71 0.000 1.09 0.277 = .3678506 -.0005469 251 75.82 0.0000 0.2334 0.2304 .00985 [95% Conf. Interval] 5828949 .001903

Source Model .007361756 .02417575 Residual Total 8_KO 8 SP500 _cons SS .031537506 df 1 .007361756 249 .000097091 250 MS Coef. Std. Err. .4753727 .0545926 .0006781 .0006219 .00012615 Number of obs F(1, 249) Prob > F R-squared Adj R-squared Root MSE t P>|t| 8.71 0.000 1.09 0.277 = .3678506 -.0005469 251 75.82 0.0000 0.2334 0.2304 .00985 [95% Conf. Interval] 5828949 .001903

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Estimate the linear regression model 9ABC = a +ß × g_SP500 and report 95% confidence level for ß coefficient.
Transcribed Image Text:Estimate the linear regression model 9ABC = a +ß × g_SP500 and report 95% confidence level for ß coefficient.
Source Model Residual Total g_KO 8 SP500 _cons SS .007361756 .02417575 .031537506 df 1 249 250 Coef. Std. Err. .4753727 .0545926 .0006781 0006219 MS .007361756 .000097091 .00012615 Number of obs F(1, 249) Prob > F R-squared Adj R-squared = Root MSE t P>|t| 8.71 0.000 1.09 0.277 = .3678506 -.0005469 251 75.82 0.0000 0.2334 0.2304 .00985 [95% Conf. Interval] .5828949 .001903
Transcribed Image Text:Source Model Residual Total g_KO 8 SP500 _cons SS .007361756 .02417575 .031537506 df 1 249 250 Coef. Std. Err. .4753727 .0545926 .0006781 0006219 MS .007361756 .000097091 .00012615 Number of obs F(1, 249) Prob > F R-squared Adj R-squared = Root MSE t P>|t| 8.71 0.000 1.09 0.277 = .3678506 -.0005469 251 75.82 0.0000 0.2334 0.2304 .00985 [95% Conf. Interval] .5828949 .001903
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