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Applied Multivariate Statistical Analysis: Pearson New International Edition
For courses in Multivariate Statistics, Marketing Research, Intermediate Business Statistics, Statistics in Education, and graduate-level courses in Experimental Design and Statistics.
Appropriate for experimental scientists in a variety of disciplines, this market-leading text offers a readable introduction to the statistical analysis of multivariate observations. Its primary goal is to impart the knowledge necessary to make proper interpretations and select appropriate techniques for analysing multivariate data. Ideal for a junior/senior or graduate level course that explores the statistical methods for describing and analysing multivariate data, the text assumes two or more statistics courses as a prerequisite.
776 pages, Paperback
First published April 28, 1982
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Displaying 1 - 9 of 9 reviews
December 19, 2019
Dense but thorough. It takes time but there is rhythm, if discovered, helps moves things along.
December 28, 2018
How this book can write more than eight hundred pages? It's very verbose and fail to hit the point. From theoretical aspect, it leave so many things that need to explain in a clear and logical way,only tell you to consult books that list in the bibliography. From practical aspect, it doesn't contain any interesting example, all of which give me a full taste of what is trivial.
I don't read the whole book, because I find that if I don't programming on my computer, I will require few useful things. The authors treat many trivial things with details,which make me disgust. I think this book can reduce to at least four hundred pages.
How can my teacher recommend this nonsensical book? I DO NOT RECOMMEND IT AT ALL!
I don't read the whole book, because I find that if I don't programming on my computer, I will require few useful things. The authors treat many trivial things with details,which make me disgust. I think this book can reduce to at least four hundred pages.
How can my teacher recommend this nonsensical book? I DO NOT RECOMMEND IT AT ALL!
December 17, 2021
The exercises & examples are fine, but I found a lot of the theoretical portions to be unmotivated and pedagogically confusing. The book, as it settles into a rhythm and focuses on a topic (say: Factor Analysis) does quite a good job, but overall it feels a bit too scattershot to be worthwhile for anything other than a possible quick dip if you need that 3rd resource to cement a concept.
June 10, 2012
I am still reading it.
Chapter One: makes the case for why we need multivariate analysis. Talks about MV representation, descriptive stats (mean, variance, corr, cov)provides an interesting intuition that corr is the normalized cov. Ath the end it provides a section on visualizing multivariate data which is pretty useless (See Maneesh Agrawala's slides for a more in dept overview)
Chapter Two: is an overview of matrix algebra and random vectors. Some of the basic operations that are going to be used is presented here. Among them is finding the projection of a vector onto another vector which is very useful. Definitions for orthogonal vectors and matrices are presented. A^-1 = A^T. Thing s like positive definiteness is defined. And a useful thing is the diagram on page 65 that starts us on learning about the spread of data that will be used in chapter 3 to set the stage for PCA.
Chapter 3: Talks about intuition and geometric interpretation of variance and SD and I very much enjoyed it. The connection between determinants of a matrix and the volume of its parallelogram is just amazingly mind blowing. The chapter is kind of important as it talks about equations for sample mean and variance. The math is just about the right amount and prepares you for later.
Chapter 4: is on Multivariate Normal and is kind of important.
Chapter 5, 6: Have not read
Chapter 7: Multivariate Linear Regression. Related to multivariate sentiment analysis
Chapter 8: is on PCA and is related to my research
Chapter 9:
Chapter One: makes the case for why we need multivariate analysis. Talks about MV representation, descriptive stats (mean, variance, corr, cov)provides an interesting intuition that corr is the normalized cov. Ath the end it provides a section on visualizing multivariate data which is pretty useless (See Maneesh Agrawala's slides for a more in dept overview)
Chapter Two: is an overview of matrix algebra and random vectors. Some of the basic operations that are going to be used is presented here. Among them is finding the projection of a vector onto another vector which is very useful. Definitions for orthogonal vectors and matrices are presented. A^-1 = A^T. Thing s like positive definiteness is defined. And a useful thing is the diagram on page 65 that starts us on learning about the spread of data that will be used in chapter 3 to set the stage for PCA.
Chapter 3: Talks about intuition and geometric interpretation of variance and SD and I very much enjoyed it. The connection between determinants of a matrix and the volume of its parallelogram is just amazingly mind blowing. The chapter is kind of important as it talks about equations for sample mean and variance. The math is just about the right amount and prepares you for later.
Chapter 4: is on Multivariate Normal and is kind of important.
Chapter 5, 6: Have not read
Chapter 7: Multivariate Linear Regression. Related to multivariate sentiment analysis
Chapter 8: is on PCA and is related to my research
Chapter 9:
May 27, 2016
If you are searching for both consistent and practice book to reference your work this is absolutely for you. The theorical fundaments and the mathematical examples are perfectly mixed, very recomendable for any student or academic, specially considering if you understand through working with matrices. The reading is simple and fluid, it doesn't discuss too much, but presents you the models from beginning to end. It also has a variety of items so you can find a lot of subjects in this text.
One of the elemental books for multivariate analysis.
One of the elemental books for multivariate analysis.
March 29, 2008
I didn't have a solid-enough background in mathematical statistics when I started this book, so the earlier theory-heavy chapters went over my head a little bit... especially Chapter 4, essentially all about proving that the multivariate normal distribution behaves pretty much just like you'd expect it to. But the rest of it is great! Lots of handy examples, explained pretty well for the most part, with SAS code examples and coverage of really interesting multivariate topics.
April 22, 2020
What I like about this book is that it's purposefully written in a reassuring way, with pretty straightforward "main" text and all the difficult math banished to Examples and Appendices. I mean, we still have to know it, but the design upholds an illusion that this subject is easier than it really is. XD Considering that I paid $100 for the book, though, I really wish it was printed on better paper: http://www.permanencematters.com/the-...
April 14, 2016
This book was a good for applied statistical analysis as: principle components , discriminant and cluster analysis. Actually, I did not read all chapters. I depended only on the fundamental analysis. Furthermore, it contained a lot of examples and exercises for practice.
Displaying 1 - 9 of 9 reviews