Open the data set, German, which is provided on the textbook website. The data set consists of 20 predictors, both continuous and categorical, and a single response variable, indicating whether the individual record represents a good or bad credit risk. The predictors are as follows, with amounts in Deutsche marks (DM): ∘ Status of existing checking account ∘ Duration in months ∘ Credit history ∘ Loan purpose ∘ Credit amount ∘ Savings account/bonds ∘ Presently employed since ∘ Payment as percentage of disposable income ∘ Personal status and gender ∘ Other debtors/guarantors ∘ Present residence since ∘ Property ∘ Age ∘ Other installment plans ∘ Housing ∘ Number of existing credits at this bank ∘ Job ∘ Number of people being liable to provide maintenance for ∘ Telephone ∘ Foreign worker. More information is available about this data set from the textbook website. Construct the best logistic regression model you can, using as many of the methods we learned in this chapter as possible. Provide strong interpretive support for your model, including explanations of derived variables, indicator variables, and so on.