I have the necessary documents.

Question 1: Evaluate the following two Model Comparisons

Comparison 1:

-2LL(C)

PC

-2LL(A)

PA

Improvement Chi Square

Probability of

Improvement Chi Square (sig)

Interpretation of Findings

(Include Improvement Chi Square and Probability of Improvement Chi Square)

Prediction Equation of

Log Odds for Model A

Using the Prediction Equation of Log Odds for Model A, what is the predicted probability of buying 'The Art History of Florence' for a customer with:

Purch = 8 Total Purchases

Last = 24 Months since last purchase

Comparison 2:

-2LL(C)

PC

-2LL(A)

PA

Improvement Chi Square

Probability of

Improvement Chi Square

Interpretation of Findings

(Include Improvement Chi Square and Probability of Improvement Chi Square)

Prediction Equation of

Log Odds for Model A

Question 2:

Which set of variables gives the best prediction of the dependent variable?

What is your evidence?

Question 3:

Using the equation with all 6 independent variables, how are the odds of buying the 'Art History of Florence' affected by:

a.# purchases of Geography books (Art) - How do the odds of buying change for an additional Geography book purchase?

b.# purchases of Do it Yourself books (Do_it) - How do the odds of buying change for an additional Do_It Yourself book purchase?

Question

Answer

3a

3b

Question 4:

Use the following cost information to assess the profitability of both modeling methods.

Cost to Mail Offer to Customer

$.55

Selling Price of 'The Art History of Florence'

$20.00

Contribution Margin

$8.00

Using the data from the Lift/Gains/Profitability Analysis that you completed in Excel, complete the following table.

Method to Identify Customers

Maximum Cumulative Operating Profit

% of Customers Receiving Mailing

Acquisition Costs per Customer

ROI = Operating Profit/ Total Fixed Costs of Campaign

Mail All Customers in Database

100%

Logistic Regression Model

Question 5:

Which of the 2 approaches would you recommend be used in the future? Why?