In this exercise, you need to construct a regression model for sales. You will be working with data from the Refrigerated Orange Juice Category. The data is from Bing Finer Foods, a major supermarket chain in the Binghamton area. The database was collected from the weekly store scanner data and contains information about sales, price, feature, display, and profit for each item in the category. These items were aggregated up from an individual UPC level. (e.g., There are 2 UPC's that comprise the Tropicana Premium 64 oz aggregate: homestyle and regular.) The data you are being provided is for a single store. There are 5 major brands in the category and they are listed in Exhibit A. The Excel data sheet is available on the course website (OJ.SAV). The variables in this worksheet are listed in Exhibit B. Question 1. A simple, but naive, demand model for Tropicana Premium 64 oz is: SALES1 = Bo + B. PRICE1 + E 1) Estimate this demand model. Attach the three output tables for model summary, ANOVA, and coefficients. 2) In this demand model, what does the B1 coefficient represent? Explain. 3) Test whether the B. coefficient is statistically significant (nonzero) at a 0.05 significance level. 4) Draw a line chart which compares the predicted and observed sales for Tropicana Premium 64 oz. & DEALS 1 CIE090 O 0 2 9109 O D 0 0 3 969 9069LO D o 4 8812SO D O 0 0 O 5 ETE090 0 O 0 o O D 7 2 0 0 0 0 o 1 8 1 9 0 2 10 1 T 1 I 11 1 12 E 1 0 0 1 13 695420 D 0 D D 14 WEEK MONTH SALESI SALESZ SALES SALES SALES PRUCE PRICE2 PRICES PRICE PRICES DEALI DEAL DEAL DEAL 1 lan 6528 8445 47040 4224 2432 05718 029531 046719 024344 o 0 0 0 0 2 lan 8352 3003 5696 2112 057188 060313 044375 046719 038906 0 0 0 6272 7776 2016 36160 05718 060313 044175 .040469 al 0 0 4 jan 6848 7968 28352 3200 1152 060313 037344 046719 038906 5 Jan 7424 7296 3712 11904 3072 057188 044375 046719 038906 0 0 0 6 Feb 6848 7200 11136 21376 4672 052188 060313 044375 037344 032694 al 0 0 7 Feb 7488 7968 35904 3648 1920 057188 060313 037344 046719 .032656 0 8 Feb 6336 7200 2366 45848 896 057188 060313 037344 031094 032656 o 0 1 1 1 9 Feb 6208 11712 45824 3328 1152 057188 .060313 031094 046719 032656 0 1 D 10 Mar 6400 10365 3005 4096 25432 057188 060313 044375 046719 029531 o 1 0 0 11 Mar 13056 7104 2816 4854 7168 051406 060313 044375 046715 .029531 o 1 1 0 0 12 Mar 8704 6432 3456 36288 7680 051406 060313 044375 03423 .629531 1 o 1 13 Mar 8832 7200 46526 3072 6912 051406 060313 037344 046739 1 1 0 14 Mar 5096 10170 4854 32704 057188 060313 037544 046719 027969 1 D 15 Apr 14208 6144 2240 9152 7212 051406 059751 044375 046719 027959 1 0 D 16 Apr 7616 8640 12:30 42944 1152 051406 059646 044375 031094 0 1 17 Apr 5632 18912 2432 4032 65536 054546 047813 044375 044790 019375 1 1 0 18 Apr 6592 6720 38208 4032 1024 054844 057188 031094 046719 02484 0 0 0 7680 8736 1725 36800 23872 054844 057188 044375 091094 .024844 0 0 1 20 May 6720 9216 11904 4224 4332 054546 057188 031014 046719 02016 1 0 1 0 21 May 10432 12576 4672 25600 59648 057188 057188 044375 031094 015469 0 0 1 22 May 13824 2008 6205 10176 16320 046719 097188 035781 031094 .024844 1 1 1 23 Jun 7872 9504 2045 29312 6080 053021 0404667 031094 03421 0 1 8064 27936 3392 13440 32832 052969 041563 038438 031094 0 1 1 25 Jun 23872 16032 20032 15290 1856 037344 041563 031094 031094 034219 1 1 26 13760 9400 54272 11008 1152 .037344 053021 026406 .031094 .034219 1 1 1 10368 14784 39485 14975 1792 040469 053021 026406 .038438 .034219 1 1 1 D 28 4608 18720 4605 37312 6336 .052969 037396 026406 .091094 .634219 ol 0 1 1 1 2626 D D 1 1 T 15 O 1 1 16 9068 0 0 o 17 1 0 O . 18 O 0 1 1 19 19 May O O 0 20 1 1 1 21 D O O 22 D D 696250 0 0 23 24 612120 O 0 1 25 0 0 26 1 O 0 O 27 27 0 28 O In this exercise, you need to construct a regression model for sales. You will be working with data from the Refrigerated Orange Juice Category. The data is from Bing Finer Foods, a major supermarket chain in the Binghamton area. The database was collected from the weekly store scanner data and contains information about sales, price, feature, display, and profit for each item in the category. These items were aggregated up from an individual UPC level. (e.g., There are 2 UPC's that comprise the Tropicana Premium 64 oz aggregate: homestyle and regular.) The data you are being provided is for a single store. There are 5 major brands in the category and they are listed in Exhibit A. The Excel data sheet is available on the course website (OJ.SAV). The variables in this worksheet are listed in Exhibit B. Question 1. A simple, but naive, demand model for Tropicana Premium 64 oz is: SALES1 = Bo + B. PRICE1 + E 1) Estimate this demand model. Attach the three output tables for model summary, ANOVA, and coefficients. 2) In this demand model, what does the B1 coefficient represent? Explain. 3) Test whether the B. coefficient is statistically significant (nonzero) at a 0.05 significance level. 4) Draw a line chart which compares the predicted and observed sales for Tropicana Premium 64 oz. & DEALS 1 CIE090 O 0 2 9109 O D 0 0 3 969 9069LO D o 4 8812SO D O 0 0 O 5 ETE090 0 O 0 o O D 7 2 0 0 0 0 o 1 8 1 9 0 2 10 1 T 1 I 11 1 12 E 1 0 0 1 13 695420 D 0 D D 14 WEEK MONTH SALESI SALESZ SALES SALES SALES PRUCE PRICE2 PRICES PRICE PRICES DEALI DEAL DEAL DEAL 1 lan 6528 8445 47040 4224 2432 05718 029531 046719 024344 o 0 0 0 0 2 lan 8352 3003 5696 2112 057188 060313 044375 046719 038906 0 0 0 6272 7776 2016 36160 05718 060313 044175 .040469 al 0 0 4 jan 6848 7968 28352 3200 1152 060313 037344 046719 038906 5 Jan 7424 7296 3712 11904 3072 057188 044375 046719 038906 0 0 0 6 Feb 6848 7200 11136 21376 4672 052188 060313 044375 037344 032694 al 0 0 7 Feb 7488 7968 35904 3648 1920 057188 060313 037344 046719 .032656 0 8 Feb 6336 7200 2366 45848 896 057188 060313 037344 031094 032656 o 0 1 1 1 9 Feb 6208 11712 45824 3328 1152 057188 .060313 031094 046719 032656 0 1 D 10 Mar 6400 10365 3005 4096 25432 057188 060313 044375 046719 029531 o 1 0 0 11 Mar 13056 7104 2816 4854 7168 051406 060313 044375 046715 .029531 o 1 1 0 0 12 Mar 8704 6432 3456 36288 7680 051406 060313 044375 03423 .629531 1 o 1 13 Mar 8832 7200 46526 3072 6912 051406 060313 037344 046739 1 1 0 14 Mar 5096 10170 4854 32704 057188 060313 037544 046719 027969 1 D 15 Apr 14208 6144 2240 9152 7212 051406 059751 044375 046719 027959 1 0 D 16 Apr 7616 8640 12:30 42944 1152 051406 059646 044375 031094 0 1 17 Apr 5632 18912 2432 4032 65536 054546 047813 044375 044790 019375 1 1 0 18 Apr 6592 6720 38208 4032 1024 054844 057188 031094 046719 02484 0 0 0 7680 8736 1725 36800 23872 054844 057188 044375 091094 .024844 0 0 1 20 May 6720 9216 11904 4224 4332 054546 057188 031014 046719 02016 1 0 1 0 21 May 10432 12576 4672 25600 59648 057188 057188 044375 031094 015469 0 0 1 22 May 13824 2008 6205 10176 16320 046719 097188 035781 031094 .024844 1 1 1 23 Jun 7872 9504 2045 29312 6080 053021 0404667 031094 03421 0 1 8064 27936 3392 13440 32832 052969 041563 038438 031094 0 1 1 25 Jun 23872 16032 20032 15290 1856 037344 041563 031094 031094 034219 1 1 26 13760 9400 54272 11008 1152 .037344 053021 026406 .031094 .034219 1 1 1 10368 14784 39485 14975 1792 040469 053021 026406 .038438 .034219 1 1 1 D 28 4608 18720 4605 37312 6336 .052969 037396 026406 .091094 .634219 ol 0 1 1 1 2626 D D 1 1 T 15 O 1 1 16 9068 0 0 o 17 1 0 O . 18 O 0 1 1 19 19 May O O 0 20 1 1 1 21 D O O 22 D D 696250 0 0 23 24 612120 O 0 1 25 0 0 26 1 O 0 O 27 27 0 28 O