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 tire sales (in thousands of tires) and advertising expenditures (in thousands of dollars). Based on the data set with 6 observations, the simple linear regression equation of the least squares line is ŷ = 3 + 1x.
∑X = 24
∑X² = 124
∑Y = 42
∑Y² = 338
∑XY = 196
MSE = 4
Using the sums of the squares given above, determine the 90 percent prediction interval for tire sales in a month when the advertising expenditure is $5,000.

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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 tire sales (in thousands of tires) and advertising expenditures (in thousands of dollars). Based on the data set with 6 observations, the simple linear regression equation of the least squares line is ŷ = 3 + 1x. ∑X = 24 ∑X² = 124 ∑Y = 42 ∑Y² = 338 ∑XY = 196 MSE = 4 Using the sums of the squares given above, determine the 90 percent prediction interval for tire sales in a month when the advertising expenditure is $5,000.

A Local Tire Dealer Wants to Predict the Number of Tires