Interpret the regression models. In particular, pay attention to the 1 values in the linear regression model(s) and the exp values (i.e., exponentiated betas or odds ratios OR)) in the logistic regression model(s). Provide a short (i.e., about two or three sentence) explanation of the association using these values, and determine significance.

Variables Entered/Removeda Variables Variables Model Entered Removed Method 1 race, Enter female a. Dependent Variable: health1 b. All requested variables entered. Model Summary Adjusted R Model R R Square Square 1 .069 .005 .002 a. Predictors: (Constant), race4, female Std. Error of the Estimate .85108 F Sig. Mean Square 1.383 1.909 .1496 ANOVA Sum of Model Squares df 1 Regression 2.765 2 2 Residual 578.024 798 Total 580.789 800 a. Dependent Variable: health1 b. Predictors: (Constant), race 4, female .724 Coefficientsa Standardized Coefficients Beta t Unstandardized Coefficients Model B Std. Error 1 (Constant) ( 1.924 .070 female -.064 .060 race4 .071 .042 a. Dependent Variable: health1 Sig. <.001 .292 .094 confidence interval for b lower bound upper .055 .155 .059 variables entered model removed method race enter female a. dependent variable: health1 b. all requested entered. summary adjusted r square .069 .005 .002 predictors: race4 std. error of the estimate .85108 f sig. mean .1496 anova sum squares df regression residual total .724 coefficientsa standardized coefficients beta t unstandardized .070 .060 .071 .042>