A local tire dealer wants to predict the number of tires sold each month. He believes that the number of tires sold is a linear function of the amount of money invested in advertising. He randomly selects 6 months of data consisting of monthly tire sales (in thousands of tires) and monthly advertising expenditures (in thousands of dollars). The simple linear regression equation is ŷ = 3 + 1x, and the sample correlation coefficient (r²) = .6364. Test to determine if there is a significant correlation between the monthly tire sales and monthly advertising expenditures. Use H₀: ρ = 0 vs. H_A: ρ ≠ 0 at α = .05.

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The Answer of A local tire dealer wants to predict the number of tires sold each month. He believes that the number of tires sold is a linear function of the amount of money invested in advertising. He randomly selects 6 months of data consisting of monthly tire sales (in thousands of tires) and monthly advertising expenditures (in thousands of dollars). The simple linear regression equation is ŷ = 3 + 1x, and the sample correlation coefficient (r²) = .6364. Test to determine if there is a significant correlation between the monthly tire sales and monthly advertising expenditures. Use H₀: ρ = 0 vs. H_A: ρ ≠ 0 at α = .05.

A Local Tire Dealer Wants to Predict the Number of Tires