Logistic Regression The LOGISTIC Procedure Model Information Data Set WORK.ICU Response Variable STA Number of Response Levels 2 Model binary logit Optimization Technique Fisher's scoring Number of Observations Read 200 Number of Observations Used 200 Response Profile Ordered Total Value STA Frequency 1 40 2 0 160 Probability modeled is STA=1. Model Convergence Status Convergence criterion (GCONV=1E-8) satisfied. Model Fit Statistics Intercept and Criterion Intercept Only Covariates AIC 202.161 194.081 SC 205.459 210.573 -2 Log L 200.161 184.081 Testing Global Null Hypothesis: BETA=0 Test Chi-Square DF Pr > ChiSq Likelihood Ratio 16.0796 4 0.0029 Score 15.9143 4 0.0031 Wald 14.0527 0.0071 Analysis of Maximum Likelihood Estimates Standard Wald Parameter DF Estimate Error Chi-Square Pr > ChiSq Intercept 1 -3.3407 0.7470 20.0024 <.0001 age can cpr inf odds ratio estimates wald effect point estimate confidence limits we conduct a study to determine the predictors of mortality among adults in an intensive care unit collect vital status individuals sta is coded as if individual and data on potential were also collected including years presence cancer prior icu admission probable infection at analyze using sas loglstic procedure observe results shown table pages based those findings answer following questions. identify dependent variable independent variables this study. state omnibus null alternative hypotheses. report test statistic pvalue that should be used hypothesis per what your conclusion about ratios intervals for all four variables. which are significant ones not sure include reasoning decisions. pvalues from maximum likelihood output. p-values. virtual compare with part question dscuss these mean public health perspective>