写数据分析作业，用R来回答问题。 RequirementThis question is based on the diabetes dataset (diabetes.arff). This dataset consists of 768 observations and 9 attributes. The brief description of the attributes are as follows: preg : Number of times the patient is pregnant plas: Plasma glucose concentration pres : Diastolic blood pressure (mm Hg) skin : Triceps skin fold thickness (mm) insu: 2-hour serum insulin (mu U/ml) mass : Body mass index (weight in kg / (height in m)^2) pedi : Diabetes pedigree function age : Age (years) class : Class variable (either tested_negative or tested_positive) a) Provide the R codes for loading the data into a variable Diabetes. b) Provide the R codes generating the CSV equivalent of the diabetes dataset (diabetes.csv). c) Compare and contrast the similarities and differences of the ARFF format and the CSV format. d) Provide the R codes for generating a logistic regression model (model) using class as the response and the ot
her attributes as predictors. e) Using the logistic regression results of the model, write down the equation of logodds of the model. Please round off all the coefficient estimates to 4 decimal places. f) We learned that logistic regression uses a logistic function: Pr(Y=REFERENCE_CLASS | data) (i.e. the probability of class = REFERENCE_CLASS given a data point. It turns out that R uses the first level value of a factor-type attribute as the reference class. g) Provide the R codes for verifying the probability value of f) using the predict() function in R. h) Suppose you want to change the reference class in R to tested_positive, you could use the relevel() function. Read the help pages and provide the R command to change reference class to test_positive so that predict() will be based on tested_positive. i) If you were to generate a new model (model2) using tested_positive as the reference class, what is the difference in the regression model of model2 compared to model?