1479 WordsJan 9, 20146 Pages

Course Project Part A
September 15, 2013
Applied Managerial Statistics
Professor Mayers
Brief Introduction
The following report presents a detailed statistical analysis of AJ Davis Department Store credit customers. Data was collected from a sample of 50 AJ Davis credit customers on five variables which are Location, Size, Income, Years, and Credit Balance. Out of the five variables, Location,Size, and Income is emphasize more in this analysis. AJ Davis Department Store is very determined to find out more information about their credit customers. So by doing a in-depth analysis of the variables and their relationships through graphical, numerical summary and interpretation should give a detailed summary of their*…show more content…*

Descriptive Statistics: Years
Variable Location Mean StDev Variance Median Range IQR Mode
Years Rural 12.46 4.94 24.44 13.00 16.00 7.00 13, 15, 18 Suburban 6.467 2.949 8.695 6.000 9.000 5.000 10 Urban 10.045 3.982 15.855 10.000 17.000 5.000 10
The 2nd pairing of variables I combined together is Location and Years. I demonstrated the variables in a dot plot to illustrate the number of years the customer has been living in that location. The most years was more than 18 years and the location was in an urban area. The highest amount of dots was 10 years. The shape of the distribution is symmetric.
The last pairing of variables I combined together is Income and Size and it demonstrated in a scatter plot. The household size of 7 or 8 has the highest income is with over $69,000 and more. The shape of distribution is positive linear relationship.
Regression Analysis: Income ($1000) versus Size
The regression equation is
Income ($1000) = 33.5 + 2.78 Size
Predictor Coef SE Coef T P
Constant 33.499 3.523 9.51 0.000
Size 2.7824 0.6844 4.07 0.000
S = 12.0983 R-Sq = 25.6% R-Sq(adj) = 24.1%
Conclusion
As the result shows, the urban location is where most of AJ Davis Department Store comes from with 44%. The urban location also has a higher credit

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