1. Logistic Regression

2. What Type of Regression?
Dependent Variable – Y
Continuous – e.g. sales, height
Dummy Variable or Multiple Regression

3. What Type of Regression?
Dependent Variable – Y
Continuous – e.g. sales, height
Dummy Variable or Multiple Regression
Binary (0 or 1) – Purchased product or didn’t purchase
Logistic Regression

4. Logistic Regression
A logistic regression can be viewed as regression where the dependent variable Y is a Dummy variable or a binary variable (0 or 1).

5. Examples
A success may be defined in terms of having a credit card client upgrade from a standard card to a premium card.
A success may be defined in terms of launching the Space Shuttle successfully and not having any damage to the secondary motors during the launch and flight.

6. Odds Ratio
Odds Ratio: a logistic regression is based on the idea of an odds ratio, the probability of a success over the probability of a failure.
pr = probability

7. Odds Ratio
Odds Ratio: a logistic regression is based on the idea of an odds ratio, the probability of a success over the probability of a failure.

8. Interpreting Odds Ratios
Equally likely to Succeed or Fail

9. Interpreting Odds Ratios
Equally likely to Succeed or Fail
Odds Ratio = 3
Three time more likely to Succeed than to Fail

10. Interpreting Odds Ratios
Equally likely to Succeed or Fail
Odds Ratio = 1/4
Four time more likely to Fail than to Succeed

11. Upgrading a Credit Card
A manager would like to know what influences the chance that a credit card customer would upgrade their credit card from a standard to a premium card
Possible Predictors of Chance Customer Upgrades
Annual Credit Card Spending
If they posses additional credit cards
Introductory offers
Gift certificate to a local restaurant
Reduced Interest rate for six months

12. Data 1 = Upgrade 1 = Additional Credit Card 1 = Reduced Interest Rate
0 = Gift Certificate

16. Model Assumption
The Model:

17. Estimating Using SPSS
Select: Analyze/Regression/Binary Logistic

18. Interpreting SPSS Output
Classification Table for UPGRADE
The Cut Value is .50
Predicted
No Upgrade Upgrade Percent Correct
N ó U
Observed ôòòòòòòòòòòòôòòòòòòòòòòòô
No Upgrade N ó ó ó %
ôòòòòòòòòòòòôòòòòòòòòòòòô
Upgrade U ó ó ó %
Overall %
Total: 18
Total: 12
Correct
=16/17
Total: 17
=11/13
Total: 13
Predicted, using model vs actual observed

19. Interpreting SPSS Output
Parameter Estimates
Variables in the Equation
Variable B S.E. Wald df Sig R Exp(B)
OTHERCAR
PROMOTIO
SPENDING
Constant

20. Interpreting SPSS Output
Hypothesis Testing
Variables in the Equation
Variable B S.E. Wald df Sig R Exp(B)
OTHERCAR
PROMOTIO
SPENDING
Constant
Wald = like t-statistic or z-statistic (Large Reject Null)
Sig. = like p-value (Small Reject Null)
Sig. for Spending Large
Remove Spending

21. Interpreting SPSS Output
Hypothesis Testing
Variables in the Equation
Variable B S.E. Wald df Sig R Exp(B)
OTHERCAR
PROMOTIO
Constant
Wald = like t-statistic or z-statistic (Large Reject Null)
Sig. = like p-value (Small Reject Null)
Sig. less than 0.05
Do not Remove any more variables

22. Model Choice
Full Model:

23. Model Choice
Full Model:
Next and Final Model:

24. Predicting Probability of Success
Customer Profile:
Spent $0 last year:

25. Predicting Probability of Success
Customer Profile:
Spent $0 last year:
Has no additional credit cards:

26. Predicting Probability of Success
Customer Profile:
Spent $0 last year:
Has no additional credit cards:
Received gift certificate promotion:

27. Predicting Probability of Success
Customer Profile:
Spent $0 last year:
Has no additional credit cards:
Received gift certificate promotion:

28. Predicting Probability of Success
Customer Profile:
Spent $0 last year:

29. Predicting Probability of Success
Customer Profile:
Spent $0 last year:
Has additional credit cards:

30. Predicting Probability of Success
Customer Profile:
Spent $0 last year:
Has additional credit cards:
Received reduce interest promotion:

31. Predicting Probability of Success
Customer Profile:
Spent $0 last year:
Has additional credit cards:
Received gift certificate promotion:

32. Space Shuttle Analysis
How does temperature influence the probability of damage occurring to the Space Shuttle’s engines?

33. Data
1 = Damage

35. SPSS Analysis Sig. for Temperature < 0.05
Variables in the Equation
Variable B S.E. Wald df Sig R Exp(B)
TEPMERATURE
Constant
Sig. for Temperature < 0.05
Temperature Influences Damage

36. Predicting Probability of Success
Launch Profile:
Temperature 36:

37. Predicting Probability of Success
Launch Profile:
Temperature 36: