A US consumer lobby wishes to develop a model to predict gasoline mileage, as me

Question

A US consumer lobby wishes to develop a model to predict gasoline mileage, as measured by miles per gallon, based on the horsepower of the car's engine. The Excel data file AUTO.xls contains data on this for fifty recent models. Use Excel Data Analysis to estimate a linear model for the relationship, the 94% confidence interval and the residual plot. State all numerical answers correct to four decimal places using the excel output results:
1. the intercept __________ ,
2. the slope coefficient _________ , and
3. the coefficient of determination,_________
4. Using this model, predict the gasoline mileage for the car with 85 horsepower. Use all decimal places in your calculation by selecting and using the values of b0 and b1 in the output generated by Excel._________
5. Does the prediction involve extrapolating the relationship? Type yes or no. ____________
Use Excel to determine a 94% confidence interval for the slope coefficient, reporting the
6. lower bound _____________
7. upper bound ________for the interval, correct to 4 decimal places

MPG Horsepower Weight 43.1 48 1985 19.9 110 3365 19.2 105 3535 17.7 165 3445 18.1 139 3205 20.3 103 2830 21.5 115 3245 16.9 155 4360 15.5 142 4054 18.5 150 3940 27.2 71 3190 41.5 76 2144 46.6 65 2110 23.7 100 2420 27.2 84 2490 39.1 58 1755 28.0 88 2605 24.0 92 2865 20.2 139 3570 20.5 95 3155 28.0 90 2678 34.7 63 2215 36.1 66 1800 35.7 80 1915 20.2 85 2965 23.9 90 3420 29.9 65 2380 30.4 67 3250 36.0 74 1980 22.6 110 2800 36.4 67 2950 27.5 95 2560 33.7 75 2210 44.6 67 1850 32.9 100 2615 38.0 67 1965 24.2 120 2930 38.1 60 1968 39.4 70 2070 25.4 116 2900 31.3 75 2542 34.1 68 1985 34.0 88 2395 31.0 82 2720 27.4 80 2670 22.3 88 2890 28.0 79 2625 17.6 85 3465 34.4 65 3465 20.6 105 3380

Explanation / Answer

SUMMARY OUTPUT Regression Statistics Multiple R 0.865688714 R Square 0.74941695 Adjusted R Square 0.738753841 Standard Error 4.176601738 Observations 50 ANOVA df SS MS F Significance F Regression 2 2451.974 1225.987 70.28128 7.51E-15 Residual 47 819.8681 17.444 Total 49 3271.842 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 58.15708245 2.658248 21.87797 2.76E-26 52.80938 63.50479 52.80938 63.50479 Horsepower -0.117525467 0.032643 -3.60028 0.000763 -0.1832 -0.05186 -0.1832 -0.05186 Weight -0.006870645 0.001401 -4.90349 1.16E-05 -0.00969 -0.00405 -0.00969 -0.00405 MPGhat = 58.15708245 -0.117525*Horsepower - 0.0068706*Weight

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