1. Discounts on Car Purchases: Does Salesperson Identity Matter?
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Assume there are five salesfolks:
Andy, Bob, Chuck, Dave and Ed
Take one (e.g., Andy) as the default (no/no/no/no) case, and add four new “dummy” variables
DB = 1 only if Bob, 0 otherwise
DC = 1 only if Chuck, 0 otherwise
DD = 1 only if Dave, 0 otherwise
DE = 1 only if Ed, 0 otherwise
The coefficient of each (in the most-complete model) will differentiate the average discount that each salesperson gives a customer from the average discount Andy would give the same customer

2. Does Salesperson Identity Matter?
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Does Salesperson Identity Matter?
Imagine that , after adding the new variables (four new columns of data) to your model, the regression yields:
Discountpred =  Age –  Income  Sex  DB + (–300)  DC + (–50)  DD  DE
With similar customers, you’d expect Bob to give a discount $240 higher than would Andy
With similar customers, you’d expect Chuck to give a discount $300 lower than would Andy, $540 lower than would Bob, and also lower than would Dave (by $250) and Ed (by $670)

3. Does “Salesperson” Interact with “Sex”?
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Does “Salesperson” Interact with “Sex”?
Are some of the salesfolk better at selling to a particular Sex of customer?
Add DB, DC, DD, DE, and DBSex, DCSex, DDSex, DESex to the model
Imagine that your regression yields:
Discountpred =  Age  Income  Sex
+ 240  DB – 350  DC + 75  DD + 10  DE
– 375  (DBSex) – 150  (DCSex) – 50  (DDSex)  (DESex)
Interpret this back in the “conceptual” model:
Discountpred =  Age –  Income  Sex
+ (240 – 375Sex)  DB + (–350 – 150Sex)  DC
+ (75 – 50Sex)  DD + ( Sex)  DE

4. Discountpred = 980 + 9. 5  Age – 0. 035  Income + 446  Sex
Discountpred =  Age –  Income  Sex (240 – 375Sex)  DB + (–350 – 150Sex)  DC (75 – 50Sex)  DD + ( Sex)  DE
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Given a male (Sex=0) customer, you’d expect Bob (DB=1) to give a greater discount (by $240-$3750 = $240) than Andy
Given a female (Sex=1) customer, you’d expect Bob to give a smaller discount (by $240-$3751 = -$135) than Andy
Chuck has been giving smaller discounts to both men and women than has Andy, and Dave and Ed have been giving larger discounts than Andy to both sexes
And we could take the same approach to investigate whether “Salesperson” interacts with Age, including also DBAge, DCAge, DDAge, DEAge in our model

5. Outliers These issues are dealt with here.
An outlier is a sample observation which fails to “fit” with the rest of the sample data. Such observations may distort the results of an entire study.
Types of outliers (three)
Identification of outliers (via “model analysis”)
Dealing with outliers (perhaps yielding a better model)
These issues are dealt with here.

6. Additional Session 4 Materials
“Unique Unicycles”, a recent EMP exam which deals with website optimization.
“Mantis Technologies”, another sample exam based on a firm converting from Microsoft Windows and Office software to Linux and an open-source office software suite, and choosing between three training programs. It comes with a 90-minute prerecorded Webex tutorial.

7. The Final Exam
On November 22, you’ll receive a dataset, together with a description of why the data was collected, and of some of the issues of interest to management. Over the following ten days, feel free to examine the data on your own, and with your study group. (In the past, some groups have found it useful to build a practice exam of their own. You’ve already seen several very-representative previous exams.)
Honor Code: Don’t discuss the dataset with anyone outside of your own study group.
Your (individual) exam will be given on December 3 (EMP105) /4 (EMP104). It will be based on the dataset sent out earlier, and will be open-notes, open- book, and open-computer, including any materials prepared by you and your group.