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optional. Discounts on Car Purchases: Does Salesperson Identity Matter?. 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 D B = 1 only if Bob, 0 otherwise
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optional Discounts on Car Purchases: Does Salesperson Identity Matter? • 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
optional 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)
optional 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 DBSex, DCSex, DDSex, DESex 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 (DBSex) – 150 (DCSex) – 50 (DDSex) + 450 (DESex) • Interpret this back in the “conceptual” model: Discountpred = 980 + 9.5 Age – 0.035 Income + 446 Sex + (240 – 375Sex) DB + (–350 – 150Sex) DC + (75 – 50Sex) DD + (10 + 450Sex) DE
optional Discountpred = 980 + 9.5 Age – 0.035 Income + 446 Sex + (240 – 375Sex) DB + (–350 – 150Sex) DC + (75 – 50Sex) DD + (10 + 450Sex) DE • Given a male (Sex=0) customer, you’d expect Bob (DB=1) to give a greater discount (by $240-$3750 = $240) than Andy • Given a female (Sex=1) customer, you’d expect Bob to give a smaller discount (by $240-$3751 = -$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 DBAge, DCAge, DDAge, DEAge in our model
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.
