10.1 Financial Condition of Banks. The file Banks.csu includes data on a sample of 20 banks. The "Financial Condition" column records the judgment of an expert on the financial condition of each bank. This outcome variable takes one of two possible values-weak or strong-according to the financial condition of the bank. The predictors are two ratios used in the financial analysis of banks: TotLns&Lses/ Assets is the ratio of total loans and leases to total assets and TotExp/Assets is the ratio of total expenses to total assets. The target is to use the two ratios for classifying the financial condition of a new bank. Run a logistic regression model (on the entire dataset) that models the status of a bank as a function of the two financial measures provided. Specify the success class as weak (this is similar to creating a dummy that is 1 for financially weak banks and 0 otherwise), and use the default cutoff value of 0.5. a. Write the estimated equation that associates the financial condition of a bank with its two predictors in three formats: i. The logit as a function of the predictors ii. The odds as a function of the predictors iii. The probability as a function of the predictors b. Consider a new bank whose total loans and leases/ assets ratio = 0.6 and total expenses/ assets ratio = 0.11. From your logistic regression model, estimate the following four quantities for this bank (use R to do all the intermediate calcula- tions; show your final answers to four decimal places): the logit, the odds, the probability of being financially weak, and the classification of the bank (use cutoff = 0.5). c. The cutoff value of 0.5 is used in conjunction with the probability of being finan- cially weak. Compute the threshold that should be used if we want to make a classification based on the odds of being financially weak, and the threshold for the corresponding logit. d. Interpret the estimated coefficient for the total loans & leases to total assets ratio (TotLns&Lses/Assets) in terms of the odds of being financially weak. e. When a bank that is in poor financial condition is misclassified as financially strong, the misclassification cost is much higher than when a financially strong bank is misclassified as weak. To minimize the expected cost of misclassification, should the cutoff value for classification (which is currently at 0.5) be increased or decreased