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Question
A major car magazine has recently collected data on 30 leading cars in the U.S. market. It is interested in building a multiple regression model to explain the variation in highway miles. The following correlation matrix has been computed from the data collected:
| mileage, highway | mileage, city | Curb Weight | cylinders | Horsepower | |
| mileage, highway | 1 | ||||
| mileage, city | 0.857550598 | 1 | |||
| Curb Weight | -0.739110566 | -0.70765104 | 1 | ||
| cylinders | -0.694837149 | -0.866135056 | 0.596475711 | 1 | |
| Horsepower | -0.549172956 | -0.684199197 | 0.293202385 | 0.840347219 | 1 |
The analysts also produced the following multiple regression output using curb weight, cylinders, and horsepower as the three independent variables. Note, a number of the output fields are missing, but can be determined from the information provided.
Based on the information provided, the 95 percent confidence interval estimate for regression slope coefficient for horsepower is approximately - 0.041 to 0.009 and since this interval crosses zero, we are unable to conclude that the regression slope coefficient for this variable is different from zero.
Answer
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