# R Dataset / Package DAAG / hills

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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. 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. 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## DescriptionThe record times in 1984 for 35 Scottish hill races. ## Usagehills ## FormatThis 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
## SourceA.C. Atkinson (1986) Comment: Aspects of diagnostic regression analysis. Statistical Science 1, 397-402. Also, in MASS library, with time in minutes. ## ReferencesA.C. Atkinson (1988) Transformations unmasked. Technometrics 30, 311-318. [ "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.r-project.org. |

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Attachment | Size |
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dataset-90278.csv | 883 bytes |