Need help in the following

\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. Error z value Pr (>|z|) (Intercept) -9. 378740 1. 305611 -7.183 6.80e-13 pregnant 0 . 033118 0 . 062612 0. 529 0. 5968 glucose 0 . 035918 0 . 006363 5. 645 1. 65e-08 pressure -0 . 002704 0 . 013176 -0. 205 0.8374 triceps 0 . 013279 0 . 018221 0.729 0. 4661 insulin -0. 000333 0 . 001426 -0.234 0 . 8154 mass 0 . 061365 0 . 029888 2 . 053 0 . 0401 pedigree 1. 212056 0. 479073 2.530 0 . 0114 age 0. 036957 0 . 020728 1. 783 0. 0746 . Signif. codes: 0 '* * 0. 001 0. 01 '*' 0.05 ' 0.1 ' '1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 402.89 on 313 degrees of freedom Residual deviance: 289.30 1 305 degrees of freedom AIC: 307.3 Number of Fisher Scoring iterations: 5Consider the output from the binary logistic model. Interpret the effects (together with their statistical significances) of glucose and pedigree on diabetes