Download Sp16-4270-HW05-Ch05

Spring 2016
Sp16-4270-HW05-Ch05-1
Due: 11:59 pm on 2/17/2016
100 points
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1. Using Multiple Linear Regression analysis for the Breakfast Cereal data (we used this file in
HW#4) the Cereal ratings were predicted. The predicted and actual ratings are given in the Excel
file.
a. Prepare a Lift Chart
b. Prepare a Decile-wise lift chart
2. Suppose that a law enforcement department has developed a mining technique to examine
communication and behavior descriptors for lie detection. The model was applied to a
validation data set of 500 cases. The model detected 450 cases as truthful of which 400 were
actually truthful. Of the remaining 50 detected as lies 15 were actually truthful.
a. Given the purpose of the project is to detect lies
b. Define “positive”, i.e., the outcome of interest.
c. Construct the classification confusion matrix.
d. What is the accuracy and misclassification rates?
e. What is the true positive rate? What is another name for this rate?
f. What is the false positive rate? What is another name for this rate?
3. The propensity of 30 records from the validation data set calculated using the data mining
model used in problem #2 above is given in the Excel file.
a. Construct an updatable classification confusion matrix.
b. Using Excel Data Table command set up a table of Accuracy, Overall error rate,
Sensitivity, 1-Specificty and average misclassification cost, with possible cutoff
probabilities of 0 to 1 in increments of 0.05. For calculating the average
misclassification cost assume the cost of falsely detecting a lie is 50 and falsely detecting
truthfulness is 5.
c. Prepare the Accuracy/Error rate plot.
d. Prepare the ROC plot.
e. Prepare the plot of Average Misclassification Cost.