Description Usage Format Details Source References Examples

Siotani et al. (1985) describe a study of Japanese rice wine (sake)
used to investigate the relationship between two subjective ratings
(`taste`

and `smell`

) and a number of physical measurements
on 30 brands of sake.

These data provide one example of a case where a multivariate regression doesn't benefit from having multiple outcome measures, using the standard tests. Barrett (2003) uses this data to illustrate influence measures for multivariate regression models.

1 |

A data frame with 30 observations on the following 10 variables.

`taste`

mean taste rating

`smell`

mean smell rating

`pH`

pH measurement

`acidity1`

one measure of acidity

`acidity2`

another measure of acidity

`sake`

Sake-meter score

`rsugar`

direct reducing sugar content

`tsugar`

total sugar content

`alcohol`

alcohol content

`nitrogen`

formol-nitrogen content

The `taste`

and `smell`

values are the mean ratings of 10 experts
on some unknown scale.

Siotani, M. Hayakawa, T. & Fujikoshi, Y. (1985).
*Modern Multivariate Statistical Analysis: A Graduate Course and Handbook*.
American Sciences Press, p. 217.

Barrett, B. E. (2003). Understanding Influence in Multivariate Regression.
*Communications in Statistics - Theory and Methods*
32 (3), 667-680.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ```
data(Sake)
# quick look at the data
boxplot(scale(Sake))
Sake.mod <- lm(cbind(taste,smell) ~ ., data=Sake)
library(car)
Anova(Sake.mod)
predictors <- colnames(Sake)[-(1:2)]
# overall multivariate regression test
linearHypothesis(Sake.mod, predictors)
heplot(Sake.mod, hypotheses=list("Regr" = predictors))
``` |

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