The sales manager of a large automotive parts distributor wants to estimate the total annual sales...

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The sales manager of a large automotive parts distributor wants to estimate the total annual sales for each of the company's regions. Five factors appear to be related to regional sales: the number of retail outlets in the region, the number of automobiles in the region registered as of April 1, the total personal income recorded in the first quarter of the year, the average age of the automobiles (years), and the number of sales supervisors in the region. The data for each region were gathered for last year. For example, see the following table. In region 1 there were 1,739 retail outlets stocking the company's automotive parts, there were 9,270,000 registered automobiles in the region as of April 1, and so on. The region's sales for that year were $37,702,000. Annual Sales ($ millions), y 37.702 24.196 32.055 3.611 17.625 45.919 29.600 8.114 20.116 12.994 Number of Retail Outlets, X1 1,739 1,221 1,846 120 1,096 2,290 1,687 241 649 1,427 Number of Automobiles Registered (millions), x2 9.27 5.86 8.81 3.81 10.31 11.62 8.96 6.28 7.77 10.92 Personal Income ($ billions), x3 85.4 60.7 68.1 20.2 33.8 95.1 69.3 16.3 34.9 15.1 Average Automob (years) 3.5 5. 4.4 4.( 3.5 4.1 4.1 5.9 5.5 4.1 d. Conduct a test of hypothesis on each of the independent variables. Would you consider eliminating "outlets" and "bosses"? Use the 0.05 significance level. (Negative amounts should be indicated by a minus sign. Round your answers to 3 decimal places.) Delete Answer is complete but not entirely correct. 0.850 "outlets" and "bosses", Critical values are and (0.240) The sales manager of a large automotive parts distributor wants to estimate the total annual sales for each of the company's regions. Five factors appear to be related to regional sales: the number of retail outlets in the region, the number of automobiles in the region registered as of April 1, the total personal income recorded in the first quarter of the year, the average age of the automobiles (years), and the number of sales supervisors in the region. The data for each region were gathered for last year. For example, see the following table. In region 1 there were 1,739 retail outlets stocking the company's automotive parts, there were 9,270,000 registered automobiles in the region as of April 1, and so on. The region's sales for that year were $37,702,000. Annual Sales ($ millions), y 37.702 24.196 32.055 3.611 17.625 45.919 29.600 8.114 20.116 12.994 Number of Retail Outlets, X1 1,739 1,221 1,846 120 1,096 2,290 1,687 241 649 1,427 Number of Automobiles Registered (millions), x2 9.27 5.86 8.81 3.81 10.31 11.62 8.96 6.28 7.77 10.92 Personal Income ($ billions), x3 85.4 60.7 68.1 20.2 33.8 95.1 69.3 16.3 34.9 15.1 Average Automob (years) 3.5 5. 4.4 4.( 3.5 4.1 4.1 5.9 5.5 4.1 d. Conduct a test of hypothesis on each of the independent variables. Would you consider eliminating "outlets" and "bosses"? Use the 0.05 significance level. (Negative amounts should be indicated by a minus sign. Round your answers to 3 decimal places.) Delete Answer is complete but not entirely correct. 0.850 "outlets" and "bosses", Critical values are and (0.240)

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