R Dataset / Package DAAG / hills
On this Rdata statistics page, you will find information about the hills data set which pertains to Scottish Hill Races Data. The hills data set is found in the DAAG R package. You can load the hills data set in R by issuing the following command at the console data("hills"). This will load the data into a variable called hills. If R says the hills 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 with the library() command. 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 hills R data set. The size of this file is about 883 bytes.
Scottish Hill Races Data
Description
The record times in 1984 for 35 Scottish hill races.
Usage
hills
Format
This data frame contains the following columns:
 dist

distance, in miles (on the map)
 climb

total height gained during the route, in feet
 time

record time in hours
Source
A.C. Atkinson (1986) Comment: Aspects of diagnostic regression analysis. Statistical Science 1, 397402.
Also, in MASS library, with time in minutes.
References
A.C. Atkinson (1988) Transformations unmasked. Technometrics 30, 311318. [ "corrects" the time for Knock Hill from 78.65 to 18.65. It is unclear if this based on the original records.]
Examples
print("Transformation  Example 6.4.3") pairs(hills, labels=c("dist\n\n(miles)", "climb\n\n(feet)", "time\n\n(hours)")) pause()pairs(log(hills), labels=c("dist\n\n(log(miles))", "climb\n\n(log(feet))", "time\n\n(log(hours))")) pause()hills0.loglm < lm(log(time) ~ log(dist) + log(climb), data = hills) oldpar < par(mfrow=c(2,2)) plot(hills0.loglm) pause() hills.loglm < lm(log(time) ~ log(dist) + log(climb), data = hills[18,]) summary(hills.loglm) plot(hills.loglm) pause()hills2.loglm < lm(log(time) ~ log(dist)+log(climb)+log(dist):log(climb), data=hills[18,]) anova(hills.loglm, hills2.loglm) pause()step(hills2.loglm) pause()summary(hills.loglm, corr=TRUE)$coef pause()summary(hills2.loglm, corr=TRUE)$coef par(oldpar) pause()print("Nonlinear  Example 6.9.4") hills.nls0 < nls(time ~ (dist^alpha)*(climb^beta), start = c(alpha = .909, beta = .260), data = hills[18,]) summary(hills.nls0) plot(residuals(hills.nls0) ~ predict(hills.nls0)) # residual plot pause()hills$climb.mi < hills$climb/5280 hills.nls < nls(time ~ alpha + beta*dist + gamma*(climb.mi^delta), start=c(alpha = 1, beta = 1, gamma = 1, delta = 1), data=hills[18,]) summary(hills.nls) plot(residuals(hills.nls) ~ predict(hills.nls)) # residual plot
Dataset imported from https://www.rproject.org.