# petrol: N. L. Prater's Petrol Refinery Data In MASS: Support Functions and Datasets for Venables and Ripley's MASS

## 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

 `1` ```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

 ```1 2 3 4 5 6 7 8``` ```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) ```

### Example output

```         Model df      AIC      BIC    logLik   Test L.Ratio p-value
pet4.lme     1  5 149.6119 156.9406 -69.80594
pet3.lme     2  7 149.3833 159.6435 -67.69166 1 vs 2 4.22855  0.1207
```

MASS documentation built on Feb. 23, 2018, 9:01 a.m.