Principal-components analysis of Brazilian Indian anthropometric data
β Scribed by Walter A. Neves; Francisco M. Salzano; Fernando J. Da Rocha
- Publisher
- John Wiley and Sons
- Year
- 1985
- Tongue
- English
- Weight
- 321 KB
- Volume
- 67
- Category
- Article
- ISSN
- 0002-9483
No coin nor oath required. For personal study only.
π SIMILAR VOLUMES
Two of the most popular descriptive multivariate methods currently employed are the principal component analysis and canonical variate analysis methods. Canonical variate analysis is the most appropriate technique to use whenever the multivariate data are grouped and to discriminate group dierences
In many large environmental datasets redundant variables can be discarded without the loss of extra variation. Principal components analysis can be used to select those variables that contain the most information. Using an environmental dataset consisting of 36 meteorological variables spanning 37 y
## Abstract Principal component analysis (PCA) and principal component regression (PCR) are routinely used for calibration of measurement devices and for data evaluation. However, their use is hindered in some applications, e.g. hyperspectral imaging, by excessive data sets that imply unacceptable