Develop a logistic regression model using the training data. Provide final logistic regression model (with only significant variables), equation for calculating probability that specimen is genuine, confusion matrix for both training & testing data, misclassification error for both training & testing data, and discuss performance of the model. PART 3 (30 points) Develop logistic regression models with 60%%/40%, 70%/30%, and 80%%/20% partitioning into training and testing data sets using set. seed(222). Summarize training and testing accuracy, sensitivity and specificity for each in the table below and compare with 50%%/50% performance using the table below. Recommend and comment on the best model for future use based on model accuracy. (No need to reproduce codes here as they are similar to part 1 and 2. Also for splitting data in to train and test, use codes provided in Part-1 for consistency in results.) Partitioning Accuracy % Sensitivity % Specificity % Training - 30% Testing - 50% Training - 60% Testing - 40%% Training - 70% Testing - 30% Training - 80% Testing - 20% PART 4 (30 points) Compare the best and the worst logistic regression model in the previous question using ROC curve, AUC and best threshold values based on testing data. Discuss your results. 2