Simple Regression Models Case Study: Mystery Shoppers

Simple Regression Models Case Study: Mystery Shoppers Chic Sales is A H

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 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 Initial Survey Score Final Average 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

Paper For Above instruction

The decline in customer satisfaction and sales at Chic Sales, a high-end consignment store, prompted the implementation of a mystery shopper program designed to evaluate and enhance store performance across multiple locations. By analyzing the relationship between initial survey scores and final average customer satisfaction ratings, this study aims to determine if initial scores can effectively predict final outcomes. Using simple linear regression analysis on the provided data of seven stores, the study assesses the strength and significance of the relationship, introduces the concept of predictive modeling, and demonstrates how such a model could be used to forecast customer satisfaction scores based on initial assessments, including a hypothetical initial score of 90.

Data from seven store locations provided initial survey scores and final average satisfaction ratings. These data points serve as the basis for statistical analysis. First, descriptive statistics provide an overview of the data, revealing the central tendency and variability in initial and final scores. Next, a scatter plot visually assesses the relationship between initial scores and final averages, indicating whether a linear pattern exists. The core of the analysis involves calculating the Pearson correlation coefficient to measure the strength and direction of the linear relationship.

The key statistical technique employed is simple linear regression, which models the final average customer satisfaction score as a function of the initial survey score. The regression equation takes the form:

Final Average = b0 + b1 * Initial Survey Score + e

where b0 represents the intercept, b1 the slope coefficient indicating the expected change in the final average for each one-point increase in the initial score, and e the error term.

By estimating the regression parameters using least squares, the model allows us to predict the final satisfaction score from a known initial score. The statistical significance of the slope coefficient is evaluated using a t-test, determining whether the relationship observed is unlikely to be due to random chance. An R-squared value provides insight into the proportion of variance in the final scores explained by the initial scores, indicating the model's predictive power.

The results from the regression analysis show a positive, statistically significant relationship between initial survey scores and final averages, meaning that higher initial scores tend to predict higher final scores. The significance level (p-value) associated with the slope coefficient confirms whether the relationship is statistically meaningful, with a common threshold being p

Furthermore, the regression equation can be used to predict the final customer satisfaction score if the initial score is known, such as a score of 90. Substituting this value into the regression equation yields an estimated final average, providing valuable predictive insight for the store owner to anticipate customer reactions and identify stores that may require targeted improvements.

In conclusion, the analysis demonstrates that initial survey scores are a useful predictor of final satisfaction ratings within the context of Chic Sales' mystery shopper program. The regression model offers a practical tool for forecasting store performance based on initial assessments, guiding managerial decisions aimed at enhancing customer experiences and increasing sales. This approach underscores the importance of early measurement and continuous evaluation in retail management strategies.

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