1. SPSS: Assignment 4
Presented to
Dr. Zahay
Database Marketing 455
Northern Illinois University
Prepared by
Aaron Burden
April 2, 2009

2. Model Summary
Step
-2 Log
likelihood
Cox & Snell R
Square
Nagelkerke R
Square
1 24153.991a
.117 .256
a. Estimation terminated at iteration number 6 because
parameter estimates changed by less than .001.
Analysis
Log likelihood in this model is 24153.991; smaller numbers are better, but this value is large.
What are the marketing implications for this model? Linear regression with higher value is better to measure
and compare models with different sets of predictors’ variables.
Nagelkerke R Square value indicates how well model fits the regression relationship of the selected variables. .
256 means that 25.6% of the times with the use of this model, one can predict a customer purchase of this
product. Therefore, approximately 74.4% of the variation is not explained by this model.
Classification Tablea
Observed
Predicted
Bought "Art History of Florence?"
No Yes
Percentage
Correct
Step 1 Bought "Art History of
Florence?"
No 45123 355 99.2
Yes 3838 684 15.1
Overall Percentage 91.6
a. The cut value is .500
Analysis
Classification table compares the predicted values for the dependent variable and the model. The table indicates
how the counts for the predictions are broken down. According to this model, 99.2% was correctly predicted of
the respondents who answer NO to purchasing the Art History of Florence versus on 15.1% of respondents who
answered YES. Overall, the percentage of the correct predictions was 91.6%. The marketing implications for
this data is it will enable marketers to forecast sales, and discontinue those products that are not selling
compared to other products.

3. Variables in the Equation
B S.E. Wald df Sig. Exp(B)
Step 1 last -.095 .003 1149.614 1 .000 .910
purch -.170 .017 99.486 1 .000 .844
gender1 .759 .036 450.627 1 .000 2.136
youth .073 .032 5.101 1 .024 1.076
cook -.083 .025 10.698 1 .001 .920
do_it -.352 .033 116.065 1 .000 .703
reference .420 .033 163.101 1 .000 1.522
art 1.341 .030 1964.156 1 .000 3.821
geog .760 .027 783.182 1 .000 2.139
Constant -2.189 .038 3281.961 1 .000 .112
Analysis
The variable with a (-) slope are the following:
Last: -.095
Purch: -.170
Cook: -.083
Do It: -.352
The slopes of these variables will decrease the value of the Dependent variable by the given amount. (The constant
value is -2.189).
Wald is a measure of the significance of the predictor variables, and the largest values are the following:
Art: 1964.156
Last: 1149.614
Gender 1: 450.627
The marketing implications for this model will assist marketing managers to determine which variables are the best in
the model. Also, it will enable CMO’S to adapt to any changes in a marketing strategy.
Ho: There is no relationship between the selected variables.
Ha: There is a relationship between the selected variables.
Test: Logistic Regression
Confident level: .95
Conclusion: Reject the null because there is a significant relationship between the selected variables.
Best Odd Analysis
The following values will assist marketers determine the probability of an event occurring. For example, the top five
variables had the highest ExpB value:
Art: 3.821
Geog: 2.139
Gender 1: 2.136
Reference: 1.522
Youth: 1.076
These five variables have higher odds of occurring in comparison to the remaining variables. This odds ratio is useful for
interpreting the effects of the predictor variables.