Transcript Session slides

Discounts on Car Purchases: Does
Salesperson Identity Matter?
optional
• 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
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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
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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 = 980 + 9.5  Age – 0.035  Income + 446  Sex
+ 240  DB + (–300)  DC + (–50)  DD + 370  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)
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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 = 980 + 9.5  Age - 0.035  Income + 446  Sex
+ 240  DB – 350  DC + 75  DD + 10  DE
– 375  (DBSex) – 150  (DCSex) – 50  (DDSex) + 450  (DESex)
• Interpret this back in the “conceptual” model:
Discountpred = 980 + 9.5  Age – 0.035  Income + 446  Sex
+ (240 – 375Sex)  DB + (–350 – 150Sex)  DC
+ (75 – 50Sex)  DD + (10 + 450Sex)  DE
Discountpred = 980 + 9.5  Age – 0.035  Income + 446  Sex
+ (240 – 375Sex)  DB + (–350 – 150Sex)  DC
+ (75 – 50Sex)  DD + (10 + 450Sex)  DE
• 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
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Outliers
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.
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.
The Final Exam
• In February (date to be determined), 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 as an online exam later in February. 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.