# GasolineYield

0th

Percentile

##### Estimation of Gasoline Yields from Crude Oil

Operational data of the proportion of crude oil converted to gasoline after distillation and fractionation.

Keywords
datasets
##### Usage
data("GasolineYield")
##### Details

This dataset was collected by Prater (1956), its dependent variable is the proportion of crude oil after distillation and fractionation. This dataset was analyzed by Atkinson (1985), who used the linear regression model and noted that there is indication that the error distribution is not quite symmetrical, giving rise to some unduly large and small residuals'' (p. 60).

The dataset contains 32 observations on the response and on the independent variables. It has been noted (Daniel and Wood, 1971, Chapter 8) that there are only ten sets of values of the first three explanatory variables which correspond to ten different crudes and were subjected to experimentally controlled distillation conditions. These conditions are captured in variable batch and the data were ordered according to the ascending order of temp10.

##### Format

A data frame containing 32 observations on 6 variables.

yield

proportion of crude oil converted to gasoline after distillation and fractionation.

gravity

crude oil gravity (degrees API).

pressure

vapor pressure of crude oil (lbf/in2).

temp10

temperature (degrees F) at which 10 percent of crude oil has vaporized.

temp

temperature (degrees F) at which all gasoline has vaporized.

batch

factor indicating unique batch of conditions gravity, pressure, and temp10.

##### References

Atkinson, A.C. (1985). Plots, Transformations and Regression: An Introduction to Graphical Methods of Diagnostic Regression Analysis. New York: Oxford University Press.

Cribari-Neto, F., and Zeileis, A. (2010). Beta Regression in R. Journal of Statistical Software, 34(2), 1--24. http://www.jstatsoft.org/v34/i02/.

Daniel, C., and Wood, F.S. (1971). Fitting Equations to Data. New York: John Wiley and Sons.

Ferrari, S.L.P., and Cribari-Neto, F. (2004). Beta Regression for Modeling Rates and Proportions. Journal of Applied Statistics, 31(7), 799--815.

Prater, N.H. (1956). Estimate Gasoline Yields from Crudes. Petroleum Refiner, 35(5), 236--238.

betareg

##### Aliases
• GasolineYield
##### Examples
# NOT RUN {
## IGNORE_RDIFF_BEGIN
data("GasolineYield", package = "betareg")

gy1 <- betareg(yield ~ gravity + pressure + temp10 + temp, data = GasolineYield)
summary(gy1)

## Ferrari and Cribari-Neto (2004)
gy2 <- betareg(yield ~ batch + temp, data = GasolineYield)
## Table 1
summary(gy2)
## Figure 2
par(mfrow = c(3, 2))
plot(gy2, which = 1, type = "pearson", sub.caption = "")
plot(gy2, which = 1, type = "deviance", sub.caption = "")
plot(gy2, which = 5, type = "deviance", sub.caption = "")
plot(gy2, which = 4, type = "pearson", sub.caption = "")
plot(gy2, which = 2:3)
par(mfrow = c(1, 1))

## exclude 4th observation
gy2a <- update(gy2, subset = -4)
gy2a
summary(gy2a)
## IGNORE_RDIFF_END
# }

Documentation reproduced from package betareg, version 3.1-3, License: GPL-2 | GPL-3

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