An Introduction to Applied Multivariate Analysis with R (Use R!) 2011th Edition

This book is an outstanding introduction to multivariate statistics. Everitt and Hothorn explain things very clearly, with examples that you can follow along with in R. They give commands and output so that it all makes sense even if you're not following along. They don't make too many assumptions about your level of math or R. I would say this is perfect for a graduate student or similar level intent on teaching themselves some statistics.

Indeed, introduction of multivariate measures is INTUITIVE with graphs and formulas added. Although English is foreign language to me, this book is very intuitive, not easy, to read it! And this book has CRITICAL views on multivariate measures, so it is FAST to understand them! the number of stars is 7.

The content is great, what is not good are the printed versions, it looks like photocopies. I bought Springer and it's the first time I received photocopies instead of a real printed book. I don't know if is a fake edition from book182wire or Springer delivered on that way.

I am a beginner of R.R is such a powerful language and also a handy tool.This book is just what I need.Love it.

I found the first 7 chapters (typical MVA) pretty useful: Easy to understand and provides with enough underlying mathematics. Chapter 8 on repeated measures is a disappointment: It contains quite a few errors that, I believe, the authors are unknown. Take the "timber" example on pp 235. First, the method= should be "REML" for both timber.lme and timber.lme1. The reason is because a LRT is conducted on pp 237. For comparison of models with the same mean structure ("fixed part") but with different "random" parts, REML is required. Second, the p-value for the LRT is 1, which is VERY LARGE instead of "very small" as stated in the text. Furthermore, the p-value for the LRT by anova() is incorrect! The actual p-value should be calculated from a mixture of 2 chi-squared distributions.

Very, very dense read. There is an over reliance on jargon in each chapter to the point it becomes almost indecipherable.

Perfect - exactly as expected.

brand new