In: Statistics and Probability
Mileage and Vehicle Weight (n = 73 vehicles) | ||
Vehicle | Weight | City MPG |
Acura TL | 3968 | 20 |
Audi A5 | 3583 | 22 |
BMW 4 Series 428i | 3470 | 22 |
BMW X1 sDrive28i | 3527 | 23 |
Buice LaCrosse | 3990 | 18 |
Buick Enclave | 4724 | 17 |
Buick Regal | 3692 | 21 |
Cadillac ATS | 3315 | 22 |
Cadillac CTS | 3616 | 20 |
Cadillac Escalade | 5527 | 14 |
Chevrolet Camaro 1SS | 3719 | 16 |
Chevrolet Cruze LS | 3097 | 26 |
Chevrolet Impala LTZ | 3800 | 19 |
Chevrolet Malibu 2LT | 3532 | 25 |
Chevrolet Spark LS | 2269 | 31 |
Chevrolet Suburban LTZ | 5674 | 15 |
Chrysler 200 Touring LX | 3402 | 20 |
Chrysler 300 S | 4029 | 19 |
Dodge Charger SXT | 3996 | 19 |
Dodge Dart Limited | 3242 | 23 |
Fiat 500 Sport | 2434 | 31 |
Ford Fiesta S | 2575 | 29 |
Ford Focus SE | 2960 | 27 |
Ford Mustang GT | 3618 | 15 |
Ford Taurus | 4054 | 19 |
Honda Accord LX | 3192 | 24 |
Honda CRV LX | 3305 | 23 |
Hyundai Azera Limited | 3605 | 19 |
Hyundai Genesis 5.0 | 4240 | 15 |
Hyundai Santa Fe GLS | 3933 | 18 |
Infiniti Q60 | 3633 | 19 |
Infiniti QX50 | 3790 | 17 |
Jaguar F-Type | 3477 | 20 |
Jeep Compass Limited | 3258 | 21 |
Jeep Grand Cherokee Limited | 4685 | 17 |
Kia Forte LX | 2776 | 25 |
Kia Soul | 2615 | 24 |
Kia Sportage LX | 3186 | 21 |
Land Rover Range Rover Sport | 5137 | 13 |
Lexus IS 250 | 3461 | 21 |
Lexus LS 460 | 4233 | 16 |
Lexus RX 350 | 4178 | 18 |
Lincoln MKT | 4702 | 17 |
Lincoln MKZ | 3713 | 22 |
Lincoln Navigator | 5794 | 14 |
Mazda 2 | 2306 | 28 |
Mazda CX-5 Sport | 3194 | 26 |
Mercedes-Benz C-250 | 3428 | 22 |
Mercedes-Benz CL600 | 4894 | 12 |
Mercedes-Benz ML350 | 4751 | 17 |
Mini-Cooper | 2605 | 29 |
Mitsubishi Outlander Sport SE | 3296 | 25 |
Nissan Armada SV | 5267 | 13 |
Nissan Cube S | 2789 | 25 |
Nissan Maxima SV | 3570 | 19 |
Nissan Murano SV | 4011 | 18 |
Nissan Versa S | 2363 | 27 |
Porsche Cayenne | 4398 | 15 |
Scion FR-S | 2806 | 25 |
Scion iQ | 2127 | 36 |
Scion XD | 2665 | 27 |
Suburu Forester 2.5i Limited | 3419 | 24 |
Suburu Legacy 2.5i Limited | 3427 | 24 |
Toyota Camry XLE | 3280 | 25 |
Toyota Land Cruiser | 5765 | 13 |
Toyota RAV4 XLE | 3465 | 24 |
Toyota Yaris | 2295 | 30 |
Volkswagen Beetle 2.5L | 3038 | 22 |
Volkswagen Jetta SE | 3070 | 25 |
Volkswagen Toureg V6 Sport | 4711 | 17 |
Volkswagen Passat SE | 3230 | 24 |
Volvo S60 T5 | 3528 | 21 |
Volvo XC90 | 4667 | 16 |
a. Interpret the slope. Does the intercept have meaning, given the range of the data?
b. Based on the R2 and ANOVA table for your model, how would you assess the fit? Interpret the p-value for the Fstatistic. Would you say that your model’s fit is good enough to be of practical value?
c. Is an autocorrelation test appropriate for your data? If so, perform an eyeball inspection of residual plot against observation order or a runs test.
d. Use MegaStat or Minitab to generate 95 percent confidence and prediction intervals for various X-values.
e. Use MegaStat or Minitab to identify observations with high leverage.
Minitab output:
a. As weight is increased by 1 unit, expected City MPG is decreased by 0.00517 unit. Intercept has no real interpretation since when weight is zero the expected City MPG is 40.1 unit which is meaningless.
b. R-sq=80% i.e. 80% of total variation in City MPG is explained by this regression equation. Moreover from ANOVA table, we see that p-value<0.05, so the these two variables are linearly related significantly. Hence we can conclude that the fitting is good.
c.
From the above plot, we see the residuals bounce randomly around the residual = 0 line as we would hope so. In general, residuals exhibiting normal random noise around the residual = 0 line suggest that there is no serial correlation.
d.
e.
Unusual Observations: