AJ Davis department store has asked to take a look at a sample of customers (sample size: 50) in order to more efficiently generate sales and also appeal to their more profitable segment. In order to better understand their customers a statistical analysis and interpretation must be performed. AJ Davis is hoping to achieve insight on how to improve their department store operations and strengthen their customer loyalty. We have taken both quantifiable and qualitative information and have performed descriptive and inferential statistics on the data collected. We do have to disclaim that these are only from a select few from the customer base and does not represent the entire population. Therefore, the interpretations based on the sample group need to be kept in perspective and must not assume complete representation of all customers. After the collection of information from their sample set we have been able to perform statistical analysis and the results are reported below.
We have 5 variables that we have to account for and all 5 are correlated. However, we are only going to address 3 of them: Location, Income, and Years.
The first variable is Location it shows us what area the customer is from rural, urban, or suburban. This variable is a qualitative as it has no numerical form in which to record. The pie chart that was used to show the different areas accounts for the frequency of how often the customer from that area appears in the sample. The reason a pie chart is useful in this situation is that it is a good visual representation of the comparable locations. The chart shows us that the majority of customers are from the urban area, the second highest is suburban, and the lowest amount of customers are from rural areas. One could assume that the department store is in an urban area, which is more accessible for people living in the relative area. This would also address why the second highest is suburban and the least is...
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