Consider the iris data in library(datasets). Please set.seed(1) for all your results. (a) Extract the data corresponding to the flower types versicolor and virginica, a total of 100 flowers. Set aside the first 10 observations for each flower type as test data and use the remaining data consisting of 80 observations (with flower types as class labels) as training data. (b) Use Linear Discriminant Analysis (LDA) for classifying the test data. Use Sepal.Length and Sepal. Width as the predictor variables (or features). (i). Report the class-specific means of the predictor variables for the training data. (ii). Compute the confusion matrix for the test data, and the misclassification error rate. (c) Fit a logistic regression model to the training data, using the variables Sepal.Length and Sepal.Width as predictors. (i). Obtain the estimates and their standard errors for the model parameters. (ii). Compute the confusion matrix for the test data, and the misclassification error rate. (iii). Are both the predictor variables necessary for the purpose of classification? Justify.