Please Help with Part D and F using r studio. Also, please post the code you used for D and F.

1. This problem deals with the dataset wbca in the faraway package that was used as an example in lecture 2. There are 681 cases of potentially cancerous tumors of which 238 are actually malignant. Determining whether a tumor is really malignant is traditionally determined by an invasive surgical procedure. The purpose of this study was to determine whether a new procedure called fine needle aspiration which draws only a small sample of tissue could be effective in determining tumor status. (a) Fit a binomial regression with Class as the response and the other nine variables as predictors using glm in R. Report the residual deviance and associated degrees of freedom (b) What value does your model from part a) assume for the dispersion parameter d? Using one of the methods from class, compute an estimate of . Does the assumed value seem plausible? (c) Starting with your fitted model, use AIC to select a subset of predictors by backward selection. Use the step function to do this. (d) Using your reduced model from part c), estimate both the log odds and probability that a specific tumor (call it Tumor A) is benign if the predictor variables have the values Adhes-1, BNuc1 = 1, Chrom-3, Epith-2. Mitos = 1, NNuc1 = 1, Thick-4. UShap 1, and USize -1. You may use the predict.glm function to do this. (e) Suppose we have data for another tumor (Tumor B) with the same predictor values as in part d), except that Chrom- 4. Without doing any further computation, state whether the log odds of Tumor B being benign is higher or lower than Tumor A, and by how much (f) Suppose that a tumor is classified as benign if p > .5 and malignant if p