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\fobservations: 392 variables: 9 $ pregnant 1, 0, 3, 2, 1, 5, 0, 1, 1, 3, 11, 10, 1, 13, 3, 3, 4, $ glucose 89, 137, 78, 197, 189, 166, 118, 103, 115, 126, 143, $ pressure 66, 40, 50, 70, 60, 72, 84, 30, 70, 88, 94, 70, 66, 8 triceps 23, 35, 32, 45, 23, 19, 47, 38, 30, 41, 33, 26, 15, 1 S insulin 94, 168, 88, 543, 846, 175, 230, 83, 96, 235, 146, 11 mass 28.1, 43.1, 31.0, 30.5, 30.1, 25.8, 45.8, 43.3, 34.6, $ pedigree 0.167, 2. 288, 0.248, 0.158, 0. 398, 0.587, 0.551, 0.18 $ age 21, 33, 26, 53, 59, 51, 31, 33, 32, 27, 51, 41, 22, 5 $ diabetes 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1,The intention is to t a binaryr logistic regression to predict the outcome of diabetes based on other observed characteristics. The results are obtained as follows: Call: glmformula = diabetes ~ ., family = "binomial", data = train) Deviance Residuals: Min 1Q Median 3Q Max 2.3800 D.6365 0.3863 0.218? 2.2281 Coefficients: Estimate Std. (Intercept) 9.3?8?40 1 pregnant 0.033118 0 glucose 0.035918 0 pressure 0.002?04 0 triceps 0.013229 0. insulin 0.000333 0 mass 0.061365 0 pedigree 1.212056 0 age 0.03695? 0 Signif. codes: 0 '***' 0. (Dispersion parameter for Null deviance: 402.89 Residual deviance: 289.30 AIC: 302.3 .305611 .062612 .006363 .0131?6 018221 .001426 .029888 .4?90?3 .020228 001 '**' binomial on 313 on 305 Error 2 value Pr(>|z|) ?.133 6.80e13 *** 0.529 0.5963 5.645 1.65e08 *** 0.205 0.8324 0.?29 0.4661 0.234 0.8154 2.053 0.0401 * 2.530 0.0114 * 1.283 0.0?46 . 0.01 '*' 0.05 '.' 0.1 ' Family taken to be 1) degrees of freedom degrees of freedom Number of Fisher Scoring iterations: 5 Which variables show significant positive effects on diabetes at 5% level of signicance? 1