edit and revise to ensure accuracy as well: 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 log-odds of the 'Financial Condition' for a one-unit increase in the predictor, holding other predictors constant. Negative coefficients mean that as the factor increases, the likelihood of being in a weak condition decreases, and vice versa for positive coefficients. The standard errors associated with each coefficient estimate provide a measure of uncertainty in the estimation. Larger standard errors compared to the coefficient estimates suggest greater uncertainty. 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