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ASSIGNMENT 5
BUSINESS ANALYTICS II – WINTER 2016
► mba specialization
► soap sales
► pizza sales
█ MBA Specialization
Two competing MBA programs K and B you have the following information:
Program K teaches state-of-the-art statistics and is believed to have high value and salary added
Program B is believed (especially by the participants in program K) to have low value added
You have interviewed 100 recent graduates from both programs, and collected data in the file valueMBA.dta on:
preMBA
income in year before beginning the program (in $1000)
postMBA
income in year after completing the program (in $1000)
school
dummy equal to 1 for those attending Program K
Using the data provided please answer the following questions:
i.
Can you prove that on average students from Program B have higher salaries after graduation than
students from Program K?
ii.
A friend of yours claims that students in Program K specialize mainly in consulting, while students in
Program B specialize mainly in finance. She further claims that, on average, the salaries in the
financial industry are higher than the salaries in consulting. If these claims are correct, what can you
say about the estimated difference (K minus B) in the average post-graduation salaries between the
programs for people with the same specialization?
ASSIGNMENT 5 – MBA SPECIALIZATION
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ASSIGNMENT 5
BUSINESS ANALYTICS II – WINTER 2016
► mba specialization
► soap sales
► pizza sales
█ Soap Sales
Greenfield, Inc. a manufacturer of a popular bathing soap, tried to find the relation between its product’s price and its
sales. It supplies over 2,000 retail outlets in the United Stated. It collected data from 25 of these stores during one
week and run a regression using these data. For each store in the sample, it observed the independent variable Price
(measured in dollars), and the dependent variable Sales (measured in thousands of dollars). The results were as
follows:
. regress Sales Price
--------------------------------------------------------------------Sales |
Coef. Std.Err.
t
P>|t| [95% Conf. Interval]
--------------------------------------------------------------------Price | -.2929416 .0616406 -4.75 0.000 -.4204540
-.165428
_cons | 5.8291984 .4241016 13.74 0.000 4.9518744 6.7065194
--------------------------------------------------------------------After having inspected the regression of Sales (in $1000) on Price (in $) reported in the text, the product manager
makes the following observation: “The stores that lower the price of Slippery Soap in a given week tend to run more
ads in the local newspapers that week - this is part of our marketing strategy. Everybody knows that local ads boost
sales; therefore your coefficient on Price is biased because you omitted advertising expenditures from the regression.”
You re-run the regression and include an additional explanatory variable called Ads measuring weekly local ad
expenditures. The estimated coefficient on Price in this new regression becomes -0.22. Are these findings consistent
with the story of the product manager? Why or why not?
ASSIGNMENT 5 – SOAP SALES
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ASSIGNMENT 5
BUSINESS ANALYTICS II – WINTER 2016
► mba specialization
► soap sales
► pizza sales
█ Pizza Sales
Use the file pizzasales.dta. Suppose that you work for a company that has a number of take-out pizza stores located
in Illinois. To help choose a location for a new store, you want to produce a model that predicts sales at a location
based on the total income of the surrounding neighborhood. The dataset gives sales (in thousands of dollars) and total
(not per capita) neighborhood income (in millions of dollars) for 50 different stores. There is also a dummy variable that
equals 1 if your main competitor chain also has a store in the neighborhood, and zero otherwise.
i.
Run a regression of Sales on a constant, the Competitor dummy and neighborhood Income.
ii.
Create a slope dummy CompetitorIncome = Competitor⋅Income. Run a regression of Sales on a
constant, the Competitor dummy, the neighborhood Income and the newly created slope dummy.
iii.
Can you make the connection between the two regressions through the omitted variable bias? To
be specific: let’s assume that in moving from the first regression to the second you interpret the
additional variable CompetitorIncome as one that was missing from the first regression. Thus the
first regression is the “truncated” regression while the second one (the one including the slope
dummy) is the true regression. In addition you write down the correlation regression as:
CompetitorIncome = a0 + a1⋅Income
(Ignore a possible additional correlation regression that would link CompetitorIncome and
Competitor). Use the framework presented in class to link the coefficients a0, a1 and the results from
the two regressions (truncated and real). Is there any ovb in this case for the coefficient of Income?
ASSIGNMENT 5 – PIZZA SALES
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