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The management team thinks that the simple analysis completed in Part A is a good start—but

they decide that there are several other variables that they would like to consider in this

regression analysis.

The company currently has three alternative sites for the store under examination and is trying

to assess the likely profitability of opening a store on each site. Management wants to consider

the following additional explanatory variables that it feels might logically affect profit. These

include:

• The size of each store, X2,measured in thousands of square feet. The larger the store,

other things being equal, the more customers and the more profit Casey management

might reasonably expect.

• The number of different product lines carried by the store, X3. The more product lines

carried, the more popular the store is likely to be with customers.

• The distance from the nearest major competitor measured in miles, X4.

Casey Hardware wants to develop a multiple regression using the data in Table 2 and then

recommend one of the three locations to open this year based on the results of that regression

analysis.

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Page 4 of 4

Table 2. Summary of Casey Stores Performance Versus Sales, Size,

Lines, and Distance From Nearest Competitor.

Store #

Profits

(\$000s) Sales (\$000s)

Size (000s sq

ft) Lines Distance (mi)

1 42.13 748.82 6.0 150 0.1

2 6.32 140.78 1.4 75 0.1

3 38.47 702.11 5.0 170 0.5

4 -0.32 41.54 1.0 75 0.0

5 3.65 96.85 1.2 75 0.2

6 7.77 166.93 1.5 75 0.5

7 4.31 109.05 1.3 75 0.3

8 4.53 263.92 1.1 80 0.4

9 -2.69 50.84 1.1 75 0.0

10 3.22 90.08 1.2 75 0.6

11 9.03 190.59 1.4 80 0.5

12 -2.59 91.75 1.2 75 0.0

13 6.39 141.57 1.4 80 0.3

14 24.39 377.04 3.5 160 1.2

15 13.93 198.69 1.5 100 0.7

16 2.13 62.78 1.3 75 0.1

17 17.48 265.28 2.1 110 0.9

18 7.21 91.8 1.3 85 0.3

19 15.62 231.6 2.5 120 0.9

20 33.61 548.31 4.5 200 0.5