GasolineYield {bayesbr} | R Documentation |

## Estimation of Gasoline Yields from Crude Oil

### Description

Proportion of crude oil converted to gasoline after the transformation processes.

### Usage

data("GasolineYield")

### 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`

.

### Details

This dataset were analyzed by Atkinson (1985) when he used a linear regression model and observed that the linear regression model failed to describe the data well, generating large residues.

The dataset contains 32 observations on the response and on the independent
variables. It was observed that there are only ten sets of values for the first three explanatory variables, so these sets served as conditions for controlled distillation. These conditions are listed in the variable \ code batch.

With the Needs for Closing and Needs for Certainty scales strongly correlated, the NFCCscale is a combined scale between the previous two.

### References

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

doi: 10.18637/jss.v034.i02 Cribari-Neto, F., and Zeileis, A. (2010). Beta Regression in R.
*Journal of Statistical Software*, **34**(2), 1–24.

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

doi: 10.1080/0266476042000214501 Ferrari, S.L.P., and Cribari-Neto, F. (2004).
Beta Regression for Modeling Rates and Proportions.
*Journal of Applied Statistics*, **31**(7), 799–815.

### Examples

data("GasolineYield", package = "bayesbr")
bbr = bayesbr(yield ~ temp + batch, iter = 100,
data = GasolineYield)
envelope(bbr, conf=0.95, sim = 100, resid.type="quantile")

[Package

*bayesbr* version 0.0.1.0

Index]