R Dataset / Package DAAG / hills
Attachment  Size 

dataset90278.csv  883 bytes 
Documentation 

On this Picostat.com 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. Try to load the hills data set in R by issuing the following command at the console data("hills"). This may 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 library("DAAG") followed by data("hills"). Perhaps strangley, if R gives you no output after entering a command, it means the command succeeded. If it succeeded you can see the data by typing hills at the commandline which should display the entire dataset. 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 DataDescriptionThe record times in 1984 for 35 Scottish hill races. Usagehills FormatThis data frame contains the following columns:
SourceA.C. Atkinson (1986) Comment: Aspects of diagnostic regression analysis. Statistical Science 1, 397402. Also, in MASS library, with time in minutes. ReferencesA.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.] Examplesprint("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. 
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