Please interpret and discuss the strength of the relationship between stock price returns for the firm and each of the three variables (EXMRKT,SML, &HML) In particular, please consider whether or not you think that the three variables do well in explaining and predicting stock returns.

SUMMARY OUTPUT Regression Statistics Multiple R 0.620380227 R Square 0.384871627 Adjusted R Square 0.379403819 Standard Error 0.077704743 Observations 455 ANOVA df MS F Significance F Regression 4 1.700034381 0.425009 70.38865 2.88258E-46 Residual 450 2.717112178 0.006038 Total 454 4.417146559 Coefficients Standard Error t Stat P-value Lower 95% Upper 95%% Lower 95.0% Upper 95.0% Intercept -0.002426949 0.005763066 -0.42112 0.673868 -0.013752812 0.008898915 -0.013752812 0.008898915 EXMRKT 1.384597278 0.087452277 15.8326 3.38E-45 1.212731721 1.556462836 1.212731721 1.556462836 SMB 0.17978752 0.127395627 1.411253 0.158861 -0.070576694 0.430151733 -0.070576694 0.430151733 HML 1.069430321 0.132223429 8.088055 5.68E-15 0.809578273 1.32928237 0.809578273 1.32928237 RFR -0.710889349 1.237509251 -0.57445 0.565949 -3.142903989 1.721125291 -3.142903989 1.721125291 Ret on JPM =-0.0024+1.3846"EXMRKT+0.1798"SMB+1.0694"HML-0.71089"RFRSUMMARY OUTPUT Regression Statistics EXMRKT Line Fit Plot Multiple R 0.543361416 05 R Square 0.295241629 = 1 2176x - 0,0005 Adjusted R Square 0.293685871 Y Standard Error 0.082897594 Observations 455 -0.3 Predicted Y #0.1 0.2 -Linear (Y] ANOVA -05 EXMRKT MS F Significance F Regression 1 1.304125544 1.304125544 189.7734929 2.61474E-36 Residual 453 3.113021015 0.006872011 Total 454 4.417146559 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept -0.0006-49514 0.003929962 -0.165272353 0.868803346 -0.00837273 0.007073704 -0.008372732 0.007073704 EXMRKT 1.217602621 0.088386879 13.77583003 2.61474E-36 1.043903439 1.391301804 1.043903439 1.391301804 RESIDUAL OUTPUTSUMMARY OUTPUT Regression Statistics HML Line Fit Plot Multiple R 0.132125809 0.5 Y = 0 4425x + 0 0061 R Square 0.017457229 Adjusted R Square 0.015288261 Standard Error 0.097880806 Predicted Y Observations 455 -02 10.1 $0.10 0.2 Linear (Y) ANOVA HML SS MS F Significance F Regression 1 0.07711114 0.077111 8.048632 0.004758054 Residual 453 4.340035418 0.009581 Total 454 4.417146559 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 0.006146102 0.004609897 1.33324 0.183123 -0.002913336 0.01520554 -0.002913336 0.015205539 HML 0.442512986 0.155978591 2.837011 0.004758 0.135981587 0.74904439 0.135981587 0.749044385 RESIDUAL OUTPUT Observation Predicted Y Residuals 1 0.008845431 -0.031572704 2 0.001676721 -0.074766422 3 0.010834739 0.035758243 4 0.007827651 0.064090157 5 0.002783003 0.019581214 6 -0.022218981 0.069093981 7 -0.005359236 -0.015536286SUMMARY OUTPUT Regression Statistics SMB Line Fit Plot Multiple R 0.109181233 0.4 y = 0.3575x + 0.0071 R Square 0.011920542 Adjusted R Square 0.009739351 Standard Error 0.0981562 0.4 Predicted Y Observations 455 - Linear (Y) ANOVA 0.6 SMB of MS F Significance F Regression 1 0.05265478 0.052655 5.465153 0.01983382 Residual 453 4.364491779 0.009635 Total 454 4.417146559 Coefficients Standard Error|t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 0.00706-4283 0.00460385 1.53443 0.125622 -0.001983269 0.01611184 -0.001983269 0.016111835 SMB 0.357455702 0.152904782 2.337767 0.019834 0.056964997 0.65794641 0.056964997 0.657946407 RESIDUAL OUTPUT Observation Predicted Y Residuals 1 0.000451353 -0.023178625 2 -0.016670775 -0.056418926 31 0.010817568 0.035777414 4 0.01467809 0.057239719 5 0.012998048 0.009366169 6 0.021862949 0.025012051 7 0.021076547 -0.041972069