Sapphire Coffee Jennie Garcia could not believe that her career had moved so far so fast. When she left graduate school with a master’s degree in anthropology, she intended to work at a local coffee...

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Sapphire Coffee
Jennie Garcia could not believe that her career had moved so far so fast. When she left graduate school with a master’s degree in anthropology, she intended to work at a local coffee shop until something else came along that was more related to her academic background. But after a few months, she came to enjoy the business, and in a little over a year she was promoted to store manager. When the company for whom she worked continued to grow, Jennie was given oversight of a few stores.
Now, eight years after she started as a barista, Jennie is in charge of operations and planning for the company’s southern region. As a part of her responsibilities, Jennie tracks store revenues and forecasts coffee demand. Historically, Sapphire Coffee would base its demand forecast on the number of stores, believing that each store sold approximately the same amount of coffee. This approach seemed to work well when the company had shops of similar size and layout, but as the company grew, stores became more varied. Now, some stores have drive-thru windows, a feature that top management added to some stores believing that it would increase coffee sales for customers who wanted a cup of coffee on their way to work but who were too rushed to park and enter the store.
Jennie noticed that weekly sales seemed to be more variable across stores in her region and was wondering what, if anything might explain the differences. The company’s financial vice president had also noticed the increased differences in sales across the stores and was wondering what might be happening. In an e-mail to Jennie he stated that weekly store sales are expected to average $5.00 per square foot. Thus, a 1,000-square-foot store would have average weekly sales of $5,000. He asked that Jennie analyze the stores in her region to see if this rule of thumb was a reliable measure of a store’s performance.
Jennie had been in the business long enough to know that a store’s size, although an important factor, was not the only thing that might influence sales. She had never been convinced of the efficacy of the drive-thru window, believing that it detracted from the coffee house experience that so many of Sapphire Coffee customers had come to expect. The VP of finance was expecting the analysis to be completed by the weekend. Jennie decided to randomly select weekly sales records for 53 stores, along with each store’s size, whether it was located close to a college, and whether it had a drive-thru window.

  1. Identify the primary issue of the case

  2. Identify a statistical model you might use to help analyze the case

  3. Develop a multiple regression model for Jennie. Be sure to carefully specify the dependent and independent variables.

  4. Discuss how the drive-thru and college can be included in the regression model

  5. Run the regression model you developed and interpret the results

  6. Which variables are significant

  7. Provide a short report that describes your analysis and explains in management terms the findings of your model. Be sure to explain which variables, if any, are significant explanatory variables. Provide a recommendation to management

Answered Same DayDec 23, 2021

Answer To: Sapphire Coffee Jennie Garcia could not believe that her career had moved so far so fast. When she...

Robert answered on Dec 23 2021
118 Votes
1. Primary issue of the case is” what affects the weekly coffee sales in Jennie’s region is
it store size, drive thru-window or near a college?” is Jennie
right in her approach of what
affect the sale or top management?
2. Multiple linear regression model is to be used to solve this problem. Estimation of the
linear relationship between a dependent variable and one or more independent variables
or covariates is called regression analysis.
A linear regression model that contains more than one predictor ( independent ) variable
is called a multiple regression model. Following equation is an example and fit for our
problem
Y=β0+ β1X1+ β2X2+ β3X3
X1= SIZE OF STORE
X2= DRIVE THRU
X3= NEAR A COLLEGE
3. Null hypotheses : β0= β1= β2=0
Alternate hypotheses: atleast one beta not equal to zero
Analysis is done with help of SPSS. Output of the fitted model is listed below along with
the interpretation of results. As we want to know the effect of size of store, drive thru-
window or near a college on weekly sales of coffee so it is dependent variable in our
study and rest three are independent variables.
Descriptive Statistics
Mean
Std.
Deviation N
Weekly Sales 4492.5704 468.09896 53
College Nearby .23 .423 53
Drive Thru .13 .342 53
Store Size (Sq.
Ft.)
925.81 121.897 53
Correlations
Weekly Sales
College
Nearby Drive Thru
Store Size
(Sq. Ft.)
Pearson
Correlation
Weekly Sales 1.000 .680 -.712 .810
College Nearby .680 1.000 -.211 .528
Drive Thru -.712 -.211 1.000 -.580
Store Size (Sq.
Ft.)...
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