R Dataset / Package MASS / petrol

On this R-data statistics page, you will find information about the petrol data set which pertains to N. L. Prater's Petrol Refinery Data. The petrol data set is found in the MASS R package. You can load the petrol data set in R by issuing the following command at the console data("petrol"). This will load the data into a variable called petrol. If R says the petrol data set is not found, you can try installing the package by issuing this command install.packages("MASS") and then attempt to reload the data with the library() command. 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 petrol R data set. The size of this file is about 837 bytes.

N. L. Prater's Petrol Refinery Data

Description

The yield of a petroleum refining process with four covariates. The crude oil appears to come from only 10 distinct samples.

These data were originally used by Prater (1956) to build an estimation equation for the yield of the refining process of crude oil to gasoline.

Usage

petrol

Format

The variables are as follows

No: crude oil sample identification label. (Factor.)
SG: specific gravity, degrees API. (Constant within sample.)
VP: vapour pressure in pounds per square inch. (Constant within sample.)
V10: volatility of crude; ASTM 10% point. (Constant within sample.)
EP: desired volatility of gasoline. (The end point. Varies within sample.)
Y: yield as a percentage of crude.

Source

N. H. Prater (1956) Estimate gasoline yields from crudes. Petroleum Refiner 35, 236–238.

This dataset is also given in D. J. Hand, F. Daly, K. McConway, D. Lunn and E. Ostrowski (eds) (1994) A Handbook of Small Data Sets. Chapman & Hall.

References

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

Examples

library(nlme)
Petrol <- petrol
Petrol[, 2:5] <- scale(as.matrix(Petrol[, 2:5]), scale = FALSE)
pet3.lme <- lme(Y ~ SG + VP + V10 + EP,
random = ~ 1 | No, data = Petrol)
pet3.lme <- update(pet3.lme, method = "ML")
pet4.lme <- update(pet3.lme, fixed = Y ~ V10 + EP)
anova(pet4.lme, pet3.lme)

Dataset imported from https://www.r-project.org.

Attachments: csv, json

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