Taste Ratings of Japanese Rice Wine (Sake)

Description

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.

Format

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

Details

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.

The taste and smell values are the mean ratings of 10 experts on some unknown scale.

Source

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

References

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

Examples

data(Sake)
# quick look at the data
boxplot(scale(Sake))

Sake.mod <- lm(cbind(taste,smell) ~ ., data=Sake)

library(car)
car::Anova(Sake.mod)

predictors <- colnames(Sake)[-(1:2)]                 
# overall multivariate regression test
linearHypothesis(Sake.mod, predictors)

heplot(Sake.mod, hypotheses=list("Regr" = predictors))