In the logistic regression model, the "Financial Condition" attribute is the response variable that determines if the bank is classified as strong (0) or weak (1). This binary classification simplifies the analysis by categorizing banks according to their financial strength. The predictors are 'TotCap/Assets', 'TotExp/Assets', and 'TotLns&Lses/Assets'. The coefficients represent the impact of each factor on the likelihood of a bank being in a weak financial condition. By examining the coefficients in the model summary, we can determine the direction and significance of the relationship between each predictor variable and `Financial Condition`. The coefficients for these predictors are -13.71, 3353.22, and 677.51 respectively. These coefficients represent the change in the 'Financial Condition' for a one-unit increase in the predictor while holding the other predictors at a constant. The standard errors associated with each coefficient estimate provide a measure of uncertainty in the estimation. The larger standard errors compared to the coefficient estimates suggest greater uncertainty in the model. The p-values for all predictors are 0.999, which is much higher than the typical significance level of 0.05. This suggests that there is insufficient evidence to reject the null hypothesis, meaning that the predictors do not have a significant effect on the 'Financial Condition'. The deviance residuals, which measure the difference between the observed responses and the respon