... 1, 42, 68, 80, 130, 131, 152, 174, 184, 194¥196, 206, 211, 213, 244, 276, 309¥312 Maxwell, A.E.,164, 195, 311 Mayes, B. T., 158, 309 McArdle, J.J., 299,312 McCloy, R.A., 269,312 McDonald, R. P., 247, 299, 312 McLachlan, G. J., 109,
chapter six Principal components analysis 6.1 Definition of principal components The technique of principal components analysis was first described by Karl Pearson ( 1901 ) . He apparently believed that this was the correct solution to ...
A Akaike, H., 248, 308 Allen, D. A., 113, 308 Andrews, D. F., 139, 308 Arminger, G., 213, 309 B Baker, R., 68, ... 309 Flamholtz, E. (3., 68, 309 Flury, B., 253, 309 Fusilier, M, R., 158, 309 G Ganstel', D. C., 158, 309 Gibson, (1., 68, ...
The major update with this edition is that R code has been included for each of the analyses described, although in practice any standard statistical package can be used. The original idea with this book still applies.
The major update with this edition is that R code has been included for each of the analyses described, although in practice any standard statistical package can be used. The original idea with this book still applies.
This book presents a general method for deriving higher-order statistics of multivariate distributions with simple algorithms that allow for actual calculations.
Some elementary statistical concepts. Matrix algebra. Samples from the multivariate normal population. Tests of hypotheses on means. The multivariate analysis of variance. Classification by the linear discrimination function. Inferences from...