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Car data has 105 observations and 12 variables. All variables except the 12th are standardized such that mean of each of them is 0 and standard deviation is 1. First 10 variables are various characteristics of the cars. The 11th variable y is the price. The 12th variable is a binary variable.

1 | ```
data("car")
``` |

A data frame with 105 observations on the following 12 variables.

`Weight`

weights of the cars

`Length`

overall length

`Wheel.base`

length of wheelbase

`Width`

width of car

`Frt.Leg.Room`

maximum front leg room

`Front.Hd`

distance between the car's head-liner and the head of a 5 ft. 9 in. front seat passenger

`Turning`

the radius of the turning circle

`Disp`

engine displacement

`HP`

net horsepower

`Tank`

fuel refill capacity

`y`

price

`y1`

High or low price

The data is created from car90 data of rpart package with selected 11 variables. The selected variables are Weight,Length,Wheel.base,Width,Frt.Leg.Room,Front.Hd,Turning,Disp,HP,Tank,Price. All these variables are standardized such that each of them has mean 0 and standard deviation 1. Price variable has been renamed as y. The variable y1 is a dichotomous variable created from that the data such that if price >=25000, then y1=1 else y1=0. Only complete cases are considered, so the data has 105 observations in place of 111 observations in car90 data set.

Terry Therneau, Beth Atkinson and Brian Ripley (2014). rpart: Recursive Partitioning and Regression Trees. R package version 4.1-8. http://CRAN.R-project.org/package=rpart

1 |

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