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Analysis with a regression model: car weight and gas mileage.

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One of the editors of a major automobile publication has collected data on 30 of the best-selling cars in the United States. The data are in a file called Automobiles( attached). The editor just purchased a Cadillac Sedan DeVille which weighs 4,012 pounds. His previous car was a Toyota Camry which weighs 3,241 pounds..

a) The editor often takes his entire family to visit relatives in a nearby state. The combined weight of his family is 570 lbs. Combining the weight of the editor's new Cadillac and his family, provide an estimate for the gas mileage the editor should expect to get on a trip to visit these relatives.

b) Calculate a 95% prediction interval for the average highway mileage for cars with a curb weight equal to the weight of the Cadillac after his family is inside.

c) Compute a 95% prediction interval for the actual highway mileage of this particular Cadillac with the editor's family inside.

d)Compare the prediction intervals computed in this problem to that computed in a previous problem( the question from the previous question was... The individual from whom the editor purchased the Cadillac said that this car got exceptional gas mileage. He claimed that this particular car got 29 mpg. Construct an appropriate 95% interval estimate that would indicate whether the seller of the Cadillac was stretching the truth or not. Comment on the seller's veracity. The answer to that question was...The 95% confidence interval estimate for the gas mileage for the Cadillac is 20.68 to 23.56 mpg. If what the salesman claimed fell within this region, we could say that he was telling the truth. However, he claimed that this particular car had a gas mileage of 29 mpg, which is not within the 95% confidence interval. Thus, we can say that the salesman was stretching the truth. ) Discuss why the intervals are different widths even though the same confidence level is used.

e)Suppose an editor for the publication wishes to predict the highway mileage of vehicles with a curb weight of 6,000 pounds. What cautions should be made before using this regression model to make that prediction? Discuss.