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Forecasting - Develop a Linear Regression Model

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Apperson and Fitz is a chain of clothing stores that caters to high school and college students. It publishes a quarterly catalog and operates a Web site that features provocatively attired males and females. The Web site is very expensive to maintain, and company executives are not sure whether the number of hits at the site relate to sales (i.e., people may be looking at the site's pictures only). The Web master has accumulated the following data for hits per month and orders placed at the Web site for the past 20 months:

Month | Hits (1,000s) | Orders (1,000s)
1 | 34.2 | 7.6
2 | 28.5 | 6.3
3 | 36.7 | 8.9
4 | 42.3 | 5.7
5 | 25.8 | 5.9
6 | 52.3 | 6.3
7 | 35.2 | 7.2
8 | 27.9 | 4.1
9 | 31.4 | 3.7
10 | 29.4 | 5.9
11 | 46.7 | 10.8
12 | 43.5 | 8.7
13 | 52.6 | 9.3
14 | 61.8 | 6.5
15 | 37.3 | 4.8
16 | 28.9 | 3.1
17 | 26.4 | 6.2
18 | 39.4 | 5.9
19 | 44.7 | 7.2
20 | 46.3 | 5.5

Develop a liner regression model for these data and indicate whether there appears to be a strong relationship between Web site hits and orders. What would be the forecast for orders with 50,000 hits per month?

The Steak and Chop Butcher Shop purchases steak from a local meatpacking house. The meat is purchased on Monday at $2.00 per pound, and the shop sells the steak for $3.00 per pound. Any steak left over at the end of the week is sold to a local zoo for $.50 per pound. The possible demands for steak and the probability of each are shown in the following table:

Demand (lb.) | Probability
20 | .10
21 | .20
22 | .30
23 | .30
24 | .10
___________
1.00

The shop must decide how much steak to order in a week. Using Excel, construct a pay-off table for this decision situation and determine the amount of steak that should be ordered, using expected value.

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Solution Summary

The solution attaches an excel spreadsheet including a linear regressions graph and conclusion on the number of units to be ordered based off the demand outlined in the question.

Solution provided by:
Gaurav Agarwal, MS
Education
  • BE, Bangalore University, India
  • MS, University of Wisconsin-Madison
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