# Store 24 Case Analysis Solution

Pages: 43 (4942 words) Published: June 9, 2009
Data Analysis and Decision Making Under Uncertainty Week 12 Workshop Store24 Solutions

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Data Analysis & Decision Making Under Uncertainty (2009)

Part 1: Developing a model for FutureContribution
Figure 1 Plots of each predictor variable against FutureContribution Scatterplot of FutureContribution vs CYJCWScore Correlation -0.063 50000 50000

Scatterplot of FutureContribution vs BanBoredomScore Correlation 0.164

FutureContribution

FutureContribution

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StatTools Student Version
For Academic Use Only

CYJCWScore

BanBoredomScore

Scatterplot of FutureContribution vs CrewSkill Correlation 0.773 50000 45000 40000 35000 30000 25000 20000 15000 2 2.5 3 3.5 4 4.5 5 50000

Scatterplot of FutureContribution vs ManagerSkills Correlation 0.054

FutureContribution

FutureContribution

45000 40000 35000 30000 25000 20000 15000 2 2.5 3 3.5 4 4.5

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StatTools Student Version
For Academic Use Only

CrewSkills

ManagerSkills

Scatterplot of FutureContribution vs Population Correlation 0.021 50000 45000 40000 35000 30000 25000 20000 15000 0 5000 10000 15000 20000 25000 30000 50000

Scatterplot of FutureContribution vs PerCapitaIncome Correlation -0.027

FutureContribution

FutureContribution

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StatTools Student Version
For Academic Use Only

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Population

PerCapitaIncome

Scatterplot of FutureContribution vs NumberofCompetitors Correlation 0.074 50000

FutureContribution

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NumberofCompetitors

Store24 Solutions – (2009)

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Comment: There appears to be two distinct groupings in all of the above scatter plots. In addition the auto-generated histograms bellow suggest there are two distinct groupings in FutureContribution and BanBoredomScore, as if there is one set of stores that are high performers and another set that are low performers. Figure 2 “Auto” Generated Histograms Histogram of FutureContribution

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Histogram of BanBoredomScore

Frequency

6 4 2

StatTools Student Version
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Frequency

8 6 4 2

StatTools Student Version
For Academic Use Only

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Running the multiple regression model:
Multiple Summary R R-Square Adjusted R-Square StErr of Estimate Durbin Watson

0.8184
Degrees of ANOVA Table Explained Unexplained Freedom

0.6698
Sum of Squares

0.5647
Mean of Squares

7640.56
F-Ratio

1.2284
p-Value

7 22

26047066 95 12843186 85
Standard Error

37210095 6 58378122

6.3740

0.0004

Coefficient Regression Table Constant CYJCWScore BanBoredomScore CrewSkills ManagerSkills Population PerCapitaIncome NumberofCompetitors

t-Value

p-Value

Lower Limit

Upper Limit

-26388.67 -104.64 81.96 19728.66 -2520.10 0.42 -0.13 -97.82

27281.64 349.78 95.22 3295.47 3501.50 0.26 0.22 1355.25

-0.9673 -0.2992 0.8607 5.9866 -0.7197 1.6158 -0.5714 -0.0722

0.3439 0.7676 0.3987 < 0.0001 0.4793 0.1204 0.5736 0.9431

82967.32 -830.04 -115.52 12894.27 -9781.78 -0.12 -0.59 -2908.43

30189.98 620.75 279.44 26563.06 4741.57 0.96 0.33 2712.79

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Data Analysis & Decision Making Under Uncertainty (2009)

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Conclusion: While the overall regression model is highly statistically significant (ANOVA p-value = 0.0004), the only statistically significant predictor is Crew Skills. However, before we can...

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