Question

(b) Develop an estimated regression equation showing how price is related to miles. What is the estimated regression model? Let x represent the miles. If required, round your answers to four decimal places. For subtractive or negative numbers use a minus sign even if there is a + sign before the blank. (Example: -300) ? = 16.4698 + -0.0587 x (c) Test whether each of the regression parameters ?0 and ?1 is equal to zero at a 0.01 level of significance. What are the correct interpretations of the estimated regression parameters? Are these interpretations reasonable? The input in the box below will not be graded, but may be reviewed and considered by your instructor. blank (d) How much of the variation in the sample values of price does the model estimated in part (b) explain? If required, round your answer to two decimal places. 53.87% (e) For the model estimated in part (b), calculate the predicted price and residual for each automobile in the data. Identify the two automobiles that were the biggest bargains. If required, round your answer to the nearest whole number. The best bargain is the Camry # 12 in the data set, which has 28000 miles, and sells for $ 2330 less than its predicted price. The second best bargain is the Camry # 4 in the data set, which has 47000 miles, and sells for $ 2211 less than its predicted price. (f) Suppose that you are considering purchasing a previously owned Camry that has been driven 70,000 miles. Use the estimated regression equation developed in part (b) to predict the price for this car. If required, round your answer to one decimal place. Do not round intermediate calculations. Predicted price: $ -4092.5 Is this the price you would offer the seller?

(b) Develop an estimated regression equation showing how price is related to miles. What is the estimated regression model?
Let x represent the miles.
If required, round your answers to four decimal places. For subtractive or negative numbers use a minus sign even if there is a + sign before the blank. (Example: -300)
? = 16.4698 + -0.0587 x

(c) Test whether each of the regression parameters ?0 and ?1 is equal to zero at a 0.01 level of significance. What are the correct interpretations of the estimated regression parameters? Are these interpretations reasonable?
The input in the box below will not be graded, but may be reviewed and considered by your instructor.
blank

(d) How much of the variation in the sample values of price does the model estimated in part (b) explain?
If required, round your answer to two decimal places.
53.87%

(e) For the model estimated in part (b), calculate the predicted price and residual for each automobile in the data. Identify the two automobiles that were the biggest bargains.
If required, round your answer to the nearest whole number.
The best bargain is the Camry # 12 in the data set, which has 28000 miles, and sells for $ 2330 less than its predicted price.
The second best bargain is the Camry # 4 in the data set, which has 47000 miles, and sells for $ 2211 less than its predicted price.

(f) Suppose that you are considering purchasing a previously owned Camry that has been driven 70,000 miles. Use the estimated regression equation developed in part (b) to predict the price for this car.
If required, round your answer to one decimal place. Do not round intermediate calculations.
Predicted price: $ -4092.5
Is this the price you would offer the seller?

(b) Develop an estimated regression equation showing how price is related to miles. What is the estimated regression model?
Let x represent the miles.
If required, round your answers to four decimal places. For subtractive or negative numbers use a minus sign even if there is a + sign before the blank. (Example: -300)
? = 16.4698 + -0.0587 x

(c) Test whether each of the regression parameters ?0 and ?1 is equal to zero at a 0.01 level of significance. What are the correct interpretations of the estimated regression parameters? Are these interpretations reasonable?
The input in the box below will not be graded, but may be reviewed and considered by your instructor.
blank

(d) How much of the variation in the sample values of price does the model estimated in part (b) explain?
If required, round your answer to two decimal places.
53.87%

(e) For the model estimated in part (b), calculate the predicted price and residual for each automobile in the data. Identify the two automobiles that were the biggest bargains.
If required, round your answer to the nearest whole number.
The best bargain is the Camry # 12 in the data set, which has 28000 miles, and sells for 2330 less than its predicted price.
The second best bargain is the Camry # 4 in the data set, which has 47000 miles, and sells for 2211 less than its predicted price.

(f) Suppose that you are considering purchasing a previously owned Camry that has been driven 70,000 miles. Use the estimated regression equation developed in part (b) to predict the price for this car.
If required, round your answer to one decimal place. Do not round intermediate calculations.
Predicted price: -4092.5
Is this the price you would offer the seller?

Added by Candice T.

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