Automobile Data from `Consumer Reports' 1990

The car.test.frame data frame has 60 rows and 8 columns, giving data on makes of cars taken from the April, 1990 issue of Consumer Reports.

Usage

data(car.test.frame)

Format

This data frame contains the following columns:

Price: a numeric vector giving the list price in US dollars of a standard model
Country: of origin, a factor with levels France Germany Japan Japan/USA Korea Mexico Sweden USA
Reliability: a numeric vector coded 1 to 5.
Mileage: fuel consumption miles per US gallon, as tested.
Type: a factor with levels Compact Large Medium Small Sporty Van
Weight: kerb weight in pounds.
Disp.: the engine capacity (displacement) in litres.
HP: the net horsepower of the vehicle.

Source

Consumer Reports, April, 1990, pp. 235–288 quoted in

John M. Chambers and Trevor J. Hastie eds. (1992) Statistical Models in S, Wadsworth and Brooks/Cole, Pacific Grove, CA 1992, pp. 46–47.

Examples

data(car.test.frame)
z.auto <- rpart(Mileage ~ Weight, car.test.frame)
summary(z.auto)
#> Call:
#> rpart(formula = Mileage ~ Weight, data = car.test.frame)
#>   n= 60 
#> 
#>           CP nsplit rel error    xerror       xstd
#> 1 0.59534912      0 1.0000000 1.0519643 0.18167427
#> 2 0.13452819      1 0.4046509 0.5841534 0.10773921
#> 3 0.06942684      2 0.2701227 0.4007493 0.07588970
#> 4 0.03449195      3 0.2006958 0.3278951 0.06630489
#> 5 0.01849081      4 0.1662039 0.3881004 0.08137973
#> 6 0.01571904      5 0.1477131 0.3936811 0.08892639
#> 7 0.01000000      7 0.1162750 0.3737755 0.08963280
#> 
#> Node number 1: 60 observations,    complexity param=0.5953491
#>   mean=24.58333, MSE=22.57639 
#>   left son=2 (45 obs) right son=3 (15 obs)
#>   Primary splits:
#>       Weight < 2567.5 to the right, improve=0.5953491, (0 missing)
#> 
#> Node number 2: 45 observations,    complexity param=0.1345282
#>   mean=22.46667, MSE=8.026667 
#>   left son=4 (22 obs) right son=5 (23 obs)
#>   Primary splits:
#>       Weight < 3087.5 to the right, improve=0.5045118, (0 missing)
#> 
#> Node number 3: 15 observations,    complexity param=0.06942684
#>   mean=30.93333, MSE=12.46222 
#>   left son=6 (9 obs) right son=7 (6 obs)
#>   Primary splits:
#>       Weight < 2280   to the right, improve=0.5030908, (0 missing)
#> 
#> Node number 4: 22 observations,    complexity param=0.01849081
#>   mean=20.40909, MSE=2.78719 
#>   left son=8 (6 obs) right son=9 (16 obs)
#>   Primary splits:
#>       Weight < 3637.5 to the right, improve=0.4084816, (0 missing)
#> 
#> Node number 5: 23 observations,    complexity param=0.01571904
#>   mean=24.43478, MSE=5.115312 
#>   left son=10 (15 obs) right son=11 (8 obs)
#>   Primary splits:
#>       Weight < 2747.5 to the right, improve=0.1476996, (0 missing)
#> 
#> Node number 6: 9 observations,    complexity param=0.03449195
#>   mean=28.88889, MSE=8.54321 
#>   left son=12 (3 obs) right son=13 (6 obs)
#>   Primary splits:
#>       Weight < 2337.5 to the left,  improve=0.607659, (0 missing)
#> 
#> Node number 7: 6 observations
#>   mean=34, MSE=2.666667 
#> 
#> Node number 8: 6 observations
#>   mean=18.66667, MSE=0.5555556 
#> 
#> Node number 9: 16 observations
#>   mean=21.0625, MSE=2.058594 
#> 
#> Node number 10: 15 observations
#>   mean=23.8, MSE=4.026667 
#> 
#> Node number 11: 8 observations,    complexity param=0.01571904
#>   mean=25.625, MSE=4.984375 
#>   left son=22 (3 obs) right son=23 (5 obs)
#>   Primary splits:
#>       Weight < 2650   to the left,  improve=0.6321839, (0 missing)
#> 
#> Node number 12: 3 observations
#>   mean=25.66667, MSE=0.2222222 
#> 
#> Node number 13: 6 observations
#>   mean=30.5, MSE=4.916667 
#> 
#> Node number 22: 3 observations
#>   mean=23.33333, MSE=0.2222222 
#> 
#> Node number 23: 5 observations
#>   mean=27, MSE=2.8 
#>