Question

Instruction: Read the cases below and answer the questions within 60 minutes Case Part 1 Jennie Garcia could not believe that her career had moved so far so fast. When she left gradua a master's degree in anthropology, she intended to work at a local coffee shop until something along that was more related to her academic background. But after a few months, she came to business, and in a little more than a year, she was promoted to store manager. When the comp she worked continued to grow, Jennie was given oversight of a few stores. Now, eight years after she started as a barista, Jennie was in charge of operations and plannir company's southern region. As a part of her responsibilities, Jennie tracks store revenues an coffee demand. Historically, Sapphire Coffee based its demand forecast on the number of st that each store sold approximately the same amount of coffee. This approach seemed to wor company had shops of similar size and layout, but as the company grew, stores became mon some stores had drive-thru windows, a feature that top management added to some stores be sales for customers who wanted a cup of coffee on their way to work would increase coffee rushed to park and enter the store to place an order Jennie noticed that weckly sales seemed to be more variable across stores in her region and anything, might explain the differences. The company's financial vice president ha ncreased differences in sales across stores and was wondering what might be happening. I Jennie, he stated that weekly store sales are expected to average $5.00 per square foot. Thu would have average weekly sales of $5,000. He asked that Jennie analyze the sto what, if to see if this rule of thumb was a reliable measure of a store's performance. The vice president of finance was expecting the analysis to be completed. Jennie value of 0.01 to conduct the hypothesis testing decided weekly sales records for 53 stores. The data was analyzed and the computer output is as fo Store Sales $6,000.00 $5,000.00 $4,000 00 $3,000.00 $2,000.00 $1,000.00 y 3.1109x1612.5 R 0.6563 S- 673 773 873 973 1073 1173 Store Size SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.810 0.656 0.649 277.115 53

Answer 1

Case Part 1

1.
The major issue of the case is that the weekly sales across stores in Jeanie's region is highly variable. Jeanie needs to explain the differences in sales across stores and find out whether there is relation between sales and the store size.

2.
The dependent variable is average weekly sales. By regression output, the relation between the variables is,
Weekly Sales = 1612.466 + 3.110 Sore size (sq. ft.)
There is a positive correlation between weekly sales and the store size. With unit sq. ft. increase in store size, the weekly sales is increased by $3.11

3.
The p-value for F test (0.0002) is less than the significance level of 0.05. Thus, the variable store size explain a significant amount of the variation in weekly sales.
R-square for the regression is 0.656. So, the variable store size explain 65.6% amount of the variation in weekly sales.

4.
Based on the regression equation, it appears that the weekly sales expectation is $3.11 per square foot. Thus, the compant is expectation is different than the estimated regression equation.

5.
By regression output, the relation between the variables is,
Weekly Sales = 1612.466 + 3.110 Sore size (sq. ft.)
There is a positive correlation between weekly sales and the store size. With unit sq. ft. increase in store size, the weekly sales is increased by $3.11
F test suggests that the model is significant and the store size is a significant variable in determining the weekly sales.