On this Picostat.com statistics page, you will find information about the houseprices data set which pertains to Aranda House Prices. The houseprices data set is found in the DAAG R package. You can load the houseprices data set in R by issuing the following command at the console data("houseprices"). This will load the data into a variable called houseprices. If R says the houseprices data set is not found, you can try installing the package by issuing this command install.packages("DAAG") and then attempt to reload the data. If you need to download R, you can go to the R project website. You can download a CSV (comma separated values) version of the houseprices R data set. The size of this file is about 189 bytes.

Aranda House Prices

Description

The houseprices data frame consists of the floor area, price, and the number of bedrooms for a sample of houses sold in Aranda in 1999. Aranda is a suburb of Canberra, Australia.

Usage

houseprices

Format

This data frame contains the following columns:

area

a numeric vector giving the floor area

bedrooms

a numeric vector giving the number of bedrooms

sale.price

a numeric vector giving the sale price in thousands of Australian dollars

Source

J.H. Maindonald

Examples

plot(sale.price~area, data=houseprices)
pause()coplot(sale.price~area|bedrooms, data=houseprices)
pause()print("Cross-Validation - Example 5.5.2")houseprices.lm <- lm(sale.price ~ area, data=houseprices)
summary(houseprices.lm)$sigma^2
pause()CVlm()
pause()print("Bootstrapping - Example 5.5.3")
houseprices.fn <- function (houseprices, index){
house.resample <- houseprices[index,]
house.lm <- lm(sale.price ~ area, data=house.resample)
coef(house.lm)[2]    # slope estimate for resampled data
}
require(boot)       # ensure that the boot package is loaded
houseprices.boot <- boot(houseprices, R=999, statistic=houseprices.fn)houseprices1.fn <- function (houseprices, index){
house.resample <- houseprices[index,]
house.lm <- lm(sale.price ~ area, data=house.resample)
predict(house.lm, newdata=data.frame(area=1200))
}houseprices1.boot <- boot(houseprices, R=999, statistic=houseprices1.fn)
boot.ci(houseprices1.boot, type="perc") # "basic" is an alternative to "perc"
houseprices2.fn <- function (houseprices, index){
house.resample <- houseprices[index,]
house.lm <- lm(sale.price ~ area, data=house.resample)
houseprices$sale.price-predict(house.lm, houseprices)  # resampled prediction errors
}n <- length(houseprices$area)
R <- 200   
houseprices2.boot <- boot(houseprices, R=R, statistic=houseprices2.fn)
house.fac <- factor(rep(1:n, rep(R, n)))
plot(house.fac, as.vector(houseprices2.boot$t), ylab="Prediction Errors", 
xlab="House")
pause()plot(apply(houseprices2.boot$t,2, sd)/predict.lm(houseprices.lm, se.fit=TRUE)$se.fit,
     ylab="Ratio of Bootstrap SE's to Model-Based SE's", xlab="House", pch=16)
abline(1,0)

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Dataset imported from https://www.r-project.org.