On this Picostat.com statistics page, you will find information about the kootenay data set which pertains to Waterflow Measurements of Kootenay River in Libby and Newgate. The kootenay data set is found in the robustbase R package. Try to load the kootenay data set in R by issuing the following command at the console data("kootenay"). This may load the data into a variable called kootenay. If R says the kootenay data set is not found, you can try installing the package by issuing this command install.packages("robustbase") and then attempt to reload the data with library("robustbase") followed by data("kootenay"). 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 kootenay at the command-line 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 kootenay R data set. The size of this file is about 142 bytes.
Waterflow Measurements of Kootenay River in Libby and Newgate
The original data set is the waterflow in January of the Kootenay
river, measured at two locations, namely, Libby (Montana) and Newgate
(British Columbia) for 13 consecutive years, 1931–1943.
The data set is of mostly interest because it has been used as example
in innumerous didactical situations about robust regression.
To this end, one number (in observation 4) has been modified from the
original data from originally 44.9 to 15.7 (here).
A data frame with 13 observations on the following 2 variables.
a numeric vector
a numeric vector
The original (unmodified) version of the data is easily obtainable
kootenay0 from the examples; other modified versions of the
data sets are also used in different places, see the examples below.
Original Data, p.58f of
Ezekiel and Fox (1959),
Methods of Correlation and Regression Analysis. Wiley, N.Y.
Hampel, F., Ronchetti, E., Rousseeuw, P. and Stahel, W. (1986)
Robust Statistics: The Approach Based on Influence Functions;
Rousseeuw, P. J. and Leroy, A. M. (1987)
Robust Regression & Outlier Detection, Wiley, N. Y.
plot(kootenay, main = "'kootenay' data")
points(kootenay[4,], col = 2, cex =2, pch = 3)abline(lm (Newgate ~ Libby, data = kootenay), col = "pink")
abline(lmrob(Newgate ~ Libby, data = kootenay), col = "blue")## The original version of Ezekiel & Fox:
kootenay0 <- kootenay
kootenay0[4, "Newgate"] <- 44.9
plot(kootenay0, main = "'kootenay0': the original data")
abline(lm (Newgate ~ Libby, data = kootenay0), col = "pink")
abline(lmrob(Newgate ~ Libby, data = kootenay0), col = "blue")## The version with "milder" outlier -- Hampel et al., p.310
kootenay2 <- kootenay0
kootenay2[4, "Libby"] <- 20.0 # instead of 77.6
plot(kootenay2, main = "The 'kootenay2' data",
xlim = range(kootenay[,"Libby"]))
points(kootenay2[4,], col = 2, cex =2, pch = 3)
abline(lm (Newgate ~ Libby, data = kootenay2), col = "pink")
abline(lmrob(Newgate ~ Libby, data = kootenay2), col = "blue")
Dataset imported from https://www.r-project.org.