Review "Simple Regression Models Case Study: Mystery Shoppers" for this topic's case study, a request to evaluate consignment stores from mystery shopper data.
Based on the information presented in the case study, create a regression model to determine the most appropriate recommendation.
Prepare a 250-500-word response to Mrs. Turner's questions about predicting final scores, statistical significance, and whether a store location should be closed based on the data provided. Explain your approach and the rationale for this method. Evaluate the outcomes of your regression model and the responses to Mrs. Turner's questions.
Submit a copy of the Excel spreadsheet file you used to design your regression model and to determine statistical significance.
Note: Students should use Excel's regression option to perform the regression.
Use an Excel spreadsheet file for the calculations and explanations. Cells should contain the formulas (i.e., if a formula was used to calculate the entry in that cell).
Mac users can use StatPlus:mac LE, free of charge, from AnalystSoft.
StatPlus:mac LE can be used with Excel 2011 to perform statistical functions.
Go to the AnalystSoft Web site and follow the installation instructions: https://www.analystsoft.com/en/products/statplusmacle/
Once installed, Apple users can use StatPlus:mac LE to complete homework problems that require the use of Excel's data analysis statistical functions.
Simple Regression Models Case Study: Mystery Shoppers
Chic Sales is a high-end consignment store with several locations in the metro area. The company noticed a decrease in sales over the last fiscal year. Research indicated customer satisfaction had decreased and the owner, Pat Turner, decided to create a mystery shopper program.
The mystery shopper program lasted over a 6-month period, employing several loyal and new customers assigned to each location. Surveys were on a 100-point scale and involved categories such as "Staff Attitude," "Store Cleanliness," "Product Availability," and "Display(s) Appeal."
After the mystery shopper period concludes, Mrs. Turner sends you the following e-mail:
From: Pat Turner
Sent: Thursday, July 7, 2016 8:57 a.m.
Subject: Mystery Data Shopper Stats and Store Performance?
Good morning! Welcome back from vacation J I hope you had a wonderful Fourth of July.
The last mystery shopper surveys came in and I have the final numbers. I am interested in whether there is a way to predict the final average based on the initial survey score. Also, is there a statistically significant relationship between how stores initially performed and what the overall average is?
The initial survey score and the final average data for all seven store locations is in the table below:
|
Store
|
1
|
2
|
3
|
4
|
5
|
6
|
7
|
|
Initial Survey Score
|
83
|
97
|
84
|
72
|
85
|
64
|
93
|
|
Final Average
|
78
|
98
|
92
|
75
|
88
|
70
|
93
|
Also, how good is the relationship between Initial Survey Score and the Final Average? Could I use an Initial Survey Score to predict a Final Average? In fact, could I predict a Final Average if I have an Initial Survey Score of 90?
If you could have this to me before the weekend, that would be great.
Thanks so much!
Pat Turner, Owner
Chic Sales Consignment, LLC