1.The dating website Oollama.com requires its users to create profiles based on a survey in which they rate their interest in five categories: physical fitness, music, spirituality, education, and alcohol consumption.A new Oollama customer, Erin, has reviewed the profiles of 40 prospective dates and classified whether she is interested in learning more about them.Based on Erin's classifications of these 40 profiles, Oollama has applied a logistic regression to predict Erin's interest in other profiles that she has not yet viewed.The resulting logistic regression model generated these probabilities of interest.

Using a cutoff value of 0.4 to classify a profile observation as interested or not, construct the confusion matrix for this training set.Computer sensitivity and specificity measures as well.

Probability of Probability of Observation Interested Interested Observation Interested hiatus-tad 35 1 1.000 13 0 0.412 21 1 0.99? 2 0 0235 29 1 0.999 3 0 0.219 25 1 0.999 7 0 0.168 39 1 0.??? 9 0 0.168 26 1 0.990 12 0 0.168 23 1 0381 18 0 0.168 33 1 0.?74 22 1 0.168 1 0 0.382 31 1 0.168 2'1 1 0.882 6 0 0.1 28 28 1 0.882 20 0 0.1 23 36 1 0.332 15 O 0.029 16 0 0. 791 5 0 0.020 27 1 0.791 14 0 0.01 5 30 1 0.791 19 0 0.01 1 32 1 0.791 8 0 0.11.18 34 1 0. 791 10- 0 0001 37 1 0. 791 17 0 0.1111 40 1 0. 791 4 0 0.001 38 1 0.732 11 0 0M Using a cutoff value of 0.4 to classify a prole observation as interested or not, construct the confusion matrix for this training set. Computer sensitivity and specificity measures as well. Predicted = ID Predicted = 1 Actual = 0 Actual = 1 Sensitivity = Specificity =