SUMMARY OUTPUT Regression Statistics Multiple R 0.357056623 R Square 0.127489432 Adjusted R Square 0.122063371 Standard Error 5.662140623 Observations 810 ANOVA df SS MS F Significance F Regression 5...

1. Which variables were significant in your model? (Use an alpha of 1% to draw your conclusions.)

2. In the regression model you ran, the binary variables were defined as Female = 0, Male = 1. Using ONLY the information from your regression output, write the regression equation for a model with the OPPOSITE definition of the binary variables (Male = 0, Female = 1).

Extracted text: SUMMARY OUTPUT Regression Statistics Multiple R 0.357056623 R Square 0.127489432 Adjusted R Square 0.122063371 Standard Error 5.662140623 Observations 810 ANOVA df SS MS F Significance F Regression 5 3766.35143 753.2702857 23.495762 4.4032E-22 Residual 804 25776.1085 32.05983643 Total 809 29542.4599 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept -0.015534766 0.42318834 -0.036708871 0.97072625 -0.8462192 0.81514964 -0.8462192 0.81514964 Gender (Male = 1) 0.985870743 0.4014699 2.455652934 0.01427351 0.19781787 1.77392362 0.19781787 1.77392362 Exercise -0.00742009 0.00118168 -6.279247486 5.5636E-10 -0.0097396 -0.0051005 -0.0097396 -0.0051005 Fast food 0.353544316 0.07335589 4.819576149 1.72E-06 0.20955264 0.49753599 0.20955264 0.49753599 Alcohol 0.194136078 0.08498657 2.284314702 0.02261287 0.02731432 0.36095783 0.02731432 0.36095783 Junk Food 0.330639265 0.08398493 3.936888006 8.9697E-05 0.16578365 0.49549488 0.16578365 0.49549488