robustbase (version 0.99-4-1)

steamUse: Steam Usage Data (Excerpt)

Description

The monthly use of steam (Steam) in a factory may be modeled and described as function of the operating days per month (Operating.Days) and mean outside temperature per month (Temperature).

Usage

data("steamUse", package="robustbase")

Format

A data frame with 25 observations on the following 9 variables.

Steam:

regression response \(Y\), the poinds of steam used monthly.

fattyAcid:

pounds of Real Fatty Acid in storage per month.

glycerine:

pounds of crude glycerine made.

wind:

average wind velocity in miles per hour (a numeric vector).

days:

an integer vector with number of days of that month, i.e., in \(28..31\).

op.days:

the number of operating days for the given month (integer).

freeze.d:

the number of days below 32 degrees Fahrenheit (\(= 0\)°C (C=Celsius) \(=\) freezing temperature of water).

temperature:

a numeric vector of average outside temperature in Fahrenheit (F).

startups:

the number of startups (of production in that month).

Details

Nor further information is given in Draper and Smith, about the place and exacts years of the measurements, though some educated guesses should be possible, see the examples.

References

Draper and Smith (1981) Applied Regression Analysis (2nd ed., p. 615 ff)

Examples

if (FALSE) {
if(require("aprean3")) { # show how  'steamUse'  is related to 'dsa01a'
  stm <- dsa01a
  names(stm) <- c("Steam", "fattyAcid", "glycerine", "wind",
		  "days", "op.days", "freeze.d",
		  "temperature", "wind.2", "startups")
  ## prove that wind.2 is  wind^2, "traditionally" rounded to 1 digit:
  stopifnot(all.equal(floor(0.5 + 10*stm[,"wind"]^2)/10,
                      stm[,"wind.2"], tol = 1e-14))
  ## hence drop it
  steamUse <- stm[, names(stm) != "wind.2"]
}
}
data(steamUse)
str(steamUse)
## Looking at this,
cbind(M=rep_len(month.abb, 25), steamUse[,5:8, drop=FALSE])
## one will conjecture that these were 25 months, Jan--Jan in a row,
## starting in a leap year (perhaps 1960 ?).

plot(steamUse)

summary(fm1 <- lmrob(Steam ~ temperature + op.days, data=steamUse))
## diagnoses 2 outliers: month of July, maybe company-wide summer vacations

## KS2014 alone seems not robust enough:
summary(fm.14 <- lmrob(Steam ~ temperature + op.days, data=steamUse,
         setting="KS2014"))
pairs(Steam ~ temperature+op.days, steamUse)