Store Size (Sq. Ft.) Weekly Sales 673 $3,250.05 686 $3,742.86 714 $3,803.60 729 $3,317.94 734 $3,519.31 767 $3,904.43 768 $4,410.39 793 $4,042.29 794 $4,591.28 795 $4,333.91 806 $4,358.96 832 $4,220.50 834 $4,123.10 837 $4,162.45 849 $4,475.18 855 $4,316.00 859 $4,212.32 868 $4,670.77 882 $4,206.76 883 $4,943.47 887 $4,651.18 911 $4,341.40 911 $4,108.00 921 $3,981.84 925 $4,278.31 938 $4,322.80 939 $4,675.41 952 $4,303.64 953 $4,609.10 955 $4,614.23 960 $4,950.63 961 $4,660.71 967 $4,691.67 982 $4,462.57 984 $4,977.29 998 $4,609.87 1000 $4,463.31 1001 $4,663.89 1001 $5,079.82 1006 $4,773.48 1008 $4,808.53 1013 $5,218.57 1031 $5,076.62 1037 $4,651.56 1045 $4,712.99 1050 $5,026.97 1064 $4,707.41 1076 $5,382.31 1082 $4,909.79 1092 $5,118.95 1108 $4,443.86 1134 $5,011.18 1218 $5,212.77

Store Size (Sq. Ft.) Weekly Sales 673 $3,250.05 686 $3,742.86 714 $3,803.60 729 $3,317.94 734 $3,519.31 767 $3,904.43 768 $4,410.39 793 $4,042.29 794 $4,591.28 795 $4,333.91 806 $4,358.96 832 $4,220.50 834 $4,123.10 837 $4,162.45 849 $4,475.18 855 $4,316.00 859 $4,212.32 868 $4,670.77 882 $4,206.76 883 $4,943.47 887 $4,651.18 911 $4,341.40 911 $4,108.00 921 $3,981.84 925 $4,278.31 938 $4,322.80 939 $4,675.41 952 $4,303.64 953 $4,609.10 955 $4,614.23 960 $4,950.63 961 $4,660.71 967 $4,691.67 982 $4,462.57 984 $4,977.29 998 $4,609.87 1000 $4,463.31 1001 $4,663.89 1001 $5,079.82 1006 $4,773.48 1008 $4,808.53 1013 $5,218.57 1031 $5,076.62 1037 $4,651.56 1045 $4,712.99 1050 $5,026.97 1064 $4,707.41 1076 $5,382.31 1082 $4,909.79 1092 $5,118.95 1108 $4,443.86 1134 $5,011.18 1218 $5,212.77

Store Size (Sq. Ft.) Weekly Sales 673 $3,250.05 686 $3,742.86 714 $3,803.60 729 $3,317.94 734 $3,519.31 767 $3,904.43 768 $4,410.39 793 $4,042.29 794 $4,591.28 795 $4,333.91 806 $4,358.96 832 $4,220.50 834 $4,123.10 837 $4,162.45 849 $4,475.18 855 $4,316.00 859 $4,212.32 868 $4,670.77 882 $4,206.76 883 $4,943.47 887 $4,651.18 911 $4,341.40 911 $4,108.00 921 $3,981.84 925 $4,278.31 938 $4,322.80 939 $4,675.41 952 $4,303.64 953 $4,609.10 955 $4,614.23 960 $4,950.63 961 $4,660.71 967 $4,691.67 982 $4,462.57 984 $4,977.29 998 $4,609.87 1000 $4,463.31 1001 $4,663.89 1001 $5,079.82 1006 $4,773.48 1008 $4,808.53 1013 $5,218.57 1031 $5,076.62 1037 $4,651.56 1045 $4,712.99 1050 $5,026.97 1064 $4,707.41 1076 $5,382.31 1082 $4,909.79 1092 $5,118.95 1108 $4,443.86 1134 $5,011.18 1218 $5,212.77

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 aca demic background. But after a few months, she came to enjoy the business, and in a little more than a year, she was promoted to store manager. When the company for which she worked continued to 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 mea- sure of a store's performance The vice president of finance was expecting the analysis to be completed by the weekend. Jennie decided to randomly select weekly sales records for 53 stores. The data are in the file Sap- phire Coffee-1. A full analysis needs to be sent to the corporate office by Friday 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 planning for the company's southern region. As a part of her responsibilities, Jennie tracks store reve- nues and forecasts coffee demand. Historically, Sapphire Coffee based 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 had 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 were too rushed to park and enter the store to Required Tasks: . Identify the major issue(s) of the case 2. Develop a scatter plot of the variables store size vs. weekly sales. Identify the dependent variable. Briefly describe the relationship between the two variables. 3. Fit a linear regression equation to the data. Does the variable store size explain a significant amount of the variation in weekly sales? place an order 4. Based on the estimated regression equation, does it appear 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 presi- dent had also noticed the increased differences in sales across that the $5.00 per square foot weekly sales expectation the company currently uses is a valid one? 5. Summarize your analysis and findings in a report to the company's vice president of finance 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 aca demic background. But after a few months, she came to enjoy the business, and in a little more than a year, she was promoted to store manager. When the company for which she worked continued to 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 mea- sure of a store's performance The vice president of finance was expecting the analysis to be completed by the weekend. Jennie decided to randomly select weekly sales records for 53 stores. The data are in the file Sap- phire Coffee-1. A full analysis needs to be sent to the corporate office by Friday 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 planning for the company's southern region. As a part of her responsibilities, Jennie tracks store reve- nues and forecasts coffee demand. Historically, Sapphire Coffee based 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 had 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 were too rushed to park and enter the store to Required Tasks: . Identify the major issue(s) of the case 2. Develop a scatter plot of the variables store size vs. weekly sales. Identify the dependent variable. Briefly describe the relationship between the two variables. 3. Fit a linear regression equation to the data. Does the variable store size explain a significant amount of the variation in weekly sales? place an order 4. Based on the estimated regression equation, does it appear 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 presi- dent had also noticed the increased differences in sales across that the $5.00 per square foot weekly sales expectation the company currently uses is a valid one? 5. Summarize your analysis and findings in a report to the company's vice president of finance