# Aj Davis Case Study

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The department store of AJ Davis is attempting to find out more information about their credit customers. They have taken a sampling of 50 credit customers using several different variables. The data collected for several of those variables and the relationship between variables have been interpreted with detailed statistical analysis to summarize the pertinent customer information. The first variable interpreted is location, which is categorical. AJ Davis listed customer location in three subcategories: urban, suburban, and rural. By using a pie chart and frequency table, we are able to see that the majority of AJ Davis’s credit customers, 44%, are located in the urban area. The next most populous area, by 30%, is the suburban area and…show more content…
To better understand the size of a household for AJ Davis’s credit customers we used a table of descriptive statistics, frequency table, and a bar chart. Descriptive Statistics: Size Variable Mean SE Mean StDev Variance Minimum Q1 Median Q3 Size 3.420 0.246 1.739 3.024 1.000 2.000 3.000 5.000 N for Variable Maximum Mode Mode Size 7.000 2 15 Tally for Discrete Variables: Size Size Count CumCnt 1 5 5 2 15 20 3 8 28 4 9 37 5 5 42 6 5 47 7 3 50 N= 50 This information tells us that the mean, or average, household size of the credit customer is 3.42. The median, or middle, household size is 3. There is a standard deviation of 1.739. As can be seen in the bar chart and the frequency table, the mode, or most frequent occurring household size, is 2 with 15 out of the 50 households being this size. The third variable that we interpreted was credit balance, a quantitative variable. We used a descriptive statistics table, histogram, and dotplot to understand the data…show more content…
The standard deviation is \$932. By looking at the histogram, we can see that most of the customers carry a balance between \$3750 and \$4250 and that can be narrowed down even further looking at the dotplot graph to see that maximum number of customers have a balance of \$4200. By using a bar chart, we tried to see if there is a relationship between location and income. The rural income seems to be lacking in the upper income and more condensed towards the mid to lower income level. The suburban area’s income is more widely spread out but is concentrated in the upper income. The income in the upper area is also more widely spread out but the majority of it is in the mid to upper income area. There does seem to be a relationship between location and income. The rural area does not have as high an income as the other two areas. We also looked at household size and income to see if there was a relationship between the two. Using a scatterplot shows us that there does not seem to be any specific pattern