2. Logistic Regression;[50 points) The same researcher used logistic regression to relate osteoporosis (1 = osteoporosis diagnosis; 0 = no osteoporosis diagnosis) to calcium levels in the blood (continuous variable). This is the model that was fit: Call: glmgformula = Osteog ~ Calcium, family = "binomial", data = dat2) Deviance Residuals: Min 1Q Median 3Q Max 2.4662 0.3434 0.3521 0.3581 0.4410 Coefficients: Estimate Std. Error 2 value E(>|z|) (Intercegtg 4.3783 1.0534 4.156 3.23e05 *** Calcium -0.1733 0.1115 -1.554 0.12 Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 4591.4 on 10064 degrees of freedom Residual deviance: 4589.0 on 10063 degrees of freedom AIC: 4593 Number of Fisher Scoring iterations: 5 a) What is the odds ratio for Calcium? (6 points)