EX6.WINE: WINE data for Exercise 3 Chapter 6
In profpetrie/regclass: Tools for an Introductory Class in Regression and Modeling
Description
Usage
Format
Details
Source
Description
WINE data for Exercise 3 Chapter 6
Usage
1
data("EX6.WINE")
Format
A data frame with 2700 observations on the following 12 variables.
Qualitya factor with levels High Low
fixed.aciditya numeric vector
volatile.aciditya numeric vector
citric.acida numeric vector
residual.sugara numeric vector
free.sulfur.dioxidea numeric vector
total.sulfur.dioxidea numeric vector
densitya numeric vector
pHa numeric vector
sulphatesa numeric vector
alcohola numeric vector
chloridesa factor with levels Little Lots
Details
Adapted from the wine quality dataset at the UCI data repository. In this case, the original quality metric has been recoded from a score between 0 and 10 to either High or Low, and the chlorides is treated here as a categorical variable instead of a quantitative variable.
Source
https://archive.ics.uci.edu/ml/datasets/Wine+Quality
P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. Modeling wine preferences by data mining from physicochemical properties.
In Decision Support Systems, Elsevier, 47(4):547-553, 2009.
profpetrie/regclass documentation built on May 26, 2019, 8:33 a.m.
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Description Usage Format Details Source
Description
WINE data for Exercise 3 Chapter 6
Usage
1 | data("EX6.WINE")
|
Format
A data frame with 2700 observations on the following 12 variables.
Qualitya factor with levels
HighLowfixed.aciditya numeric vector
volatile.aciditya numeric vector
citric.acida numeric vector
residual.sugara numeric vector
free.sulfur.dioxidea numeric vector
total.sulfur.dioxidea numeric vector
densitya numeric vector
pHa numeric vector
sulphatesa numeric vector
alcohola numeric vector
chloridesa factor with levels
LittleLots
Details
Adapted from the wine quality dataset at the UCI data repository. In this case, the original quality metric has been recoded from a score between 0 and 10 to either High or Low, and the chlorides is treated here as a categorical variable instead of a quantitative variable.
Source
https://archive.ics.uci.edu/ml/datasets/Wine+Quality
P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. Modeling wine preferences by data mining from physicochemical properties. In Decision Support Systems, Elsevier, 47(4):547-553, 2009.
R Package Documentation
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