On this R-data statistics page, you will find information about the stackloss data set which pertains to Brownlee's Stack Loss Plant Data. The stackloss data set is found in the datasets R package. You can load the stackloss data set in R by issuing the following command at the console data("stackloss"). This will load the data into a variable called stackloss. If R says the stackloss data set is not found, you can try installing the package by issuing this command install.packages("datasets") 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 stackloss R data set. The size of this file is about 297 bytes.
Brownlee's Stack Loss Plant Data
Operational data of a plant for the oxidation of ammonia to nitric acid.
stackloss is a data frame with 21 observations on 4 variables.
R project statistics dataset table
|Flow of cooling air
|Cooling Water Inlet Temperature
|Concentration of acid [per 1000, minus 500]
For compatibility with S-PLUS, the data sets
stack.x, a matrix with the first three (independent) variables of the data frame, and
stack.loss, the numeric vector giving the fourth (dependent) variable, are provided as well.
“Obtained from 21 days of operation of a plant for the oxidation of ammonia (NH3) to nitric acid (HNO3). The nitric oxides produced are absorbed in a countercurrent absorption tower”. (Brownlee, cited by Dodge, slightly reformatted by MM.)
Air Flow represents the rate of operation of the plant.
Water Temp is the temperature of cooling water circulated through coils in the absorption tower.
Acid Conc. is the concentration of the acid circulating, minus 50, times 10: that is, 89 corresponds to 58.9 per cent acid.
stack.loss (the dependent variable) is 10 times the percentage of the ingoing ammonia to the plant that escapes from the absorption column unabsorbed; that is, an (inverse) measure of the over-all efficiency of the plant.
Brownlee, K. A. (1960, 2nd ed. 1965) Statistical Theory and Methodology in Science and Engineering. New York: Wiley. pp. 491–500.
Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole.
Dodge, Y. (1996) The guinea pig of multiple regression. In: Robust Statistics, Data Analysis, and Computer Intensive Methods; In Honor of Peter Huber's 60th Birthday, 1996, Lecture Notes in Statistics 109, Springer-Verlag, New York.
summary(lm.stack <- lm(stack.loss ~ stack.x))
Dataset imported from https://www.r-project.org.