hills | R Documentation |

The record times in 1984 (`hills`

) for 35 Scottish hill races,
or in 2000 (`hills2000`

) for 56 hill races. The `hills2000`

dataset is the subset of `races2000`

for which `type`

is `hill`

.

```
data(hills)
data(hills2000)
```

- dist
distance, in miles (on the map)

- climb
total height gained during the route, in feet

- time
record time in hours

- timef
record time in hours for females, in the

`hills2000`

dataset.

A.C. Atkinson (1986) Comment: Aspects of diagnostic regression analysis. Statistical Science 1, 397-402.

Also, in MASS library, with time in minutes.

The Scottish Running Resource, http://www.hillrunning.co.uk

A.C. Atkinson (1988) Transformations unmasked. Technometrics 30,
311-318. [ "corrects" the time for Knock Hill, in the `hills`

dataset, from 78.65 to 18.65. It
is unclear if this based on the original records.]

```
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
```

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