You have been hired as consultants to the national marketing manager of a large discount store chain

| August 30, 2017

Question
You have been hired as consultants to the national marketing manager of a large discount store chain. The chain has recently conducted an experiment at 80 of their
stores (of similar sizes) to help understand unit sales of a particular flat screen
TV/VCR/DVD combo unit. The experiment ran one week. During this time, the
corporate office assigned to each store one of four price levels ($520, $540, $560,
$580), the number of daily TV ads to run locally (between 1 and 5) and the type of
display to use (special store-front display or nothing special).
The Excel file contains data on the following variables:
(1) Price: The unit price for each combo unit (in $).
(2) TV-Ads: The number of TV ads run daily
(3) Display: Whether the TV combo units were displayed prominently or not (Display=1
if the TV combo units were featured in a special display near the front of the store and
Display=0 if there was no special display).
(4) ComboSales: The number of TV combo units sold by the store during the
experimental week.
Your job is to analyze the data and report back to the national manager. You are to use
both graphs and numerical values to summarize the data. The list below specifies
activities you must do and results you must include in your report and presentation.
1. Obtain descriptive statistics, a boxplot, and histogram for ComboSales to help
you analyze the TV combo unit sales for all 80 stores (as a single group).
Summarize your most important findings to the manager.
2. The national manager is wondering if having a special display seems to make
any difference in TV combo unit sales. To address this question, sort the data
using Display as the key variable then again obtain descriptive statistics (this
time including 95% confidence intervals for the population mean), a boxplot, and
histogram for ComboSales, for each type of Display. Does it seem that the
display type makes a difference?
3. To investigate the combined effect (if any) of the type of display and the price of a
combo unit on the average number of combo units sold, obtain a two-way pivot
table with ComboSales as the "data" variable, Price as the row variable, and
Display as the column variable. Be sure to specify the cell contents of your pivot
table to be averages and not sums or counts. Generate a side-by-side bar chart
corresponding to your pivot table and interpret your bar chart and pivot table for
the manager.
4. To investigate the effect (if any) of the number of TV ads per week on the
average number of combo units sold, obtain a one-way pivot table with
ComboSales as the "data" variable and TV-Ads as the row variable. Be sure to
specify the cell contents of your pivot table to be averages and not sums or

counts. Generate a bar chart corresponding to your pivot table and interpret your
bar chart and pivot table for the manager
5. The national manager would like to set a weekly target of 60 units sold for these
TV combo units. Refer to your MegaStat outputs from question #2. The
distribution of TV combo unit sales in each sample is approximately moundshaped. Assume that the normal distribution is applicable here, and that the
sample mean and standard deviation are good estimates of the true population
mean and standard deviation. Use these printouts to estimate (with an
approximate probability) how likely it is that the actual TV combo unit sales will
meet or exceed this target for each of the two display types. Based on your
probabilities as well as confidence intervals for the mean ComboSales, with
which display type does it appear that the target can be more easily met?
6. Test the hypothesis, at the 5% significance level, that the mean number of TV
combo units sold using a special store-front display exceeds that when no
special display is used. State the hypotheses, the decision, and conclusion.
Clearly explain the reason for arriving at your conclusion. Does your conclusion
here agree with your findings in questions #2 and #3? Explain.
7. Obtain a 90% confidence interval for the difference in the mean number of TV
combo units sold when a special store front display is used compared to when no
special display is used. Relate this finding to your results in #6.
8. Generate three separate simple regression models to investigate the individual
effect, on the number of combo units sold, of price, number of TV ads per week,
and display type. Use r-square values as well as confidence intervals or
hypothesis tests on the regression slopes to assess the regression models and
to shed light on how price, number of TV ads per week, and display type
individually affect the number of combo units sold. Support your conclusion
with with scatter-plots of Y vs. X for each of the simple regressions.
9. Conduct a multiple regression analysis of the relationship between the number of
TV combo units sold (ComboSales) and the set of independent variables:
Price, TV-Ads and Display. Explain your regression function to the national
manager. Include in your explanation the meaning of the R Square and
Regression Standard Error statistics in the MegaStat regression output. What
additional information does your multiple regression model provide when
compared to the three separate regression models in #8 above?

10. To illustrate one use of regression, obtain a 95% interval for the predicted
number of TV combo units sold, using the sample regression function for the
following situations:
a. a store that charged $560, ran 3 TV ads daily, and used a prominent display,
b. a store that charged $560, ran 5 TV ads daily, and used a prominent display,
c. a store that charged $560, ran 3 TV ads daily, & did not use a prominent disp
Compare your answers for a, b, and c.
11. Use your insights from 1-10 above to make a set of recommendations regarding
the chain’s pricing and marketing policies.

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