Customer Reviews

Applied Linear Statistical Models

5 star:		(9)
4 star:		(3)
3 star:		(1)
2 star:		(0)
1 star:		(1)

 

 

I definitely recommend this book to anyone who wishes to understand how to implement the linear model. This book shows the theory and then gives comprehensive examples explaining when and how to apply the theory. The book made few assumptions about readers having a certain level of specialized knowledge, such as familiarity with matrix math. The math presented in the book is thoroughly explained from the ground up using concrete examples, well over 1000 pages of text. I wish all advanced mathematical, engineering and science books were written in a similar fashion, presenting abstract theories along with in-depth concrete examples.

I have used this book when it was in its first edition and only by Neter and Wasserman. An absolute must for those who use linear models (regression and ANOVA) and want the most exhaustive book out there. This book is one of the rare texts that is also extremely well written. You can not go wrong reading it and studying it on your own. If you can take only one statistics book with you, this is the one. I use the 4th edition so much, I wore my first one out and am now on the second.

The linear model lies at the heart of a wide variety of familiar statistical methods, including regression, anova, and ancova. Anyone with a professional interest in statistics must get to know it well. This book is a clear, well organized, and practical presentation of the linear model. It is not pie-in-the-sky or esoteric. It leads the reader carefully through the real world problems that are encountered in applying the model. The book can be read on several levels. It provides practical instructions for applying the model to various situations, but also quite thoughly expounds on the mathematical theory behind the model. The theory and practice are separate but parallel, so you can read as much or as little of the theory as you like, or as much or as little of the practice. This book belongs on the reference shelf of anyone who uses statistics. In a world where technical publications are priced through the sky, it is a relative bargain (1400 pages).

This book seems very difficult and long to read at first, but as soon as I got into the habit of using this book, this is really a great book to have. The terminologies are very easy to understand and yet not redundant, and it has some great reference areas in the sections that you could look up for information when you need to work on an exercise or a project. The exercises in the back of each section are ample enough so that you can have a firm grasp of the material after working on those exercise. This book is useful and is a must have for any math/stat student!

Enjoy...

Wonderful explanation of regression methods with many worked examples. The book is very detailed and covers practically everything you need to know on the topic. It also comes with a disk containing the example datasets. I used it for years in graduate school and still use it today. If you work through the book, you can teach yourself all about regression - everything you need is there (although no free lunch - this isn't exactly easy material)

This is one of the few stat books that I'm keeping. I've used it to help me through Linear Regression, Experimental Design, and Multiple Regression classes. This book will be a good one to keep, even after I graduate.

It was a bit over my head, at first. After 1/2 a semester (and after reading many text books that are much worse), I soon was able to get into the text.

I cannot recommend this book highly enough, especially if one is struggling through a grad level stats course. I spent days looking through texts and reading material on the web, this is the book that is detailed enough and explanatory enough so that I finally understood what was going on with statistics. I went from confused about stats to enjoying them, primarily due to this book. I have it tabbed and write all my class notes in this one... this is a keeper. I am looking forward to the fifth edition if they ever do one.... this text covers regression, logistic regression, correlation, anova and study design (actually covers the material for 3 different stats programs in this one text)--- over 1300 pages and all worth reading!!!(reading hint for the confused.... pay little attention to the reams of formulae and lots of attention to the editorial comments, explanations and discussions that accompany the formulae) Update: This text is now in its 5th edition. The author list has changed slightly (Kutner, Nachtsheim, Neter, Li), and the book now has a CD rom with it (isbn 0072386886).

Graduate students, and people professionally involved into dealing with data every now and then will find this book to be an indispensable comprehensive reference for most types of statistical tasks you may encounter; deep, solid, non nonsense, and all contained in one book. This is probably the only stat/math book you will need to keep.

An extremely comprehensive and well written textbook. Very useful as a reference on linear statistical models but at 1400 pages not a straightforward read. Each section is supported by a number of well explained examples.I would strongly recommend this book as a reference but there are a number of alternatives which I prefer in terms of readability and accessibility of the material.

I was only interested in regression, when younger, as this seemed to be useful in physics labs and in scientific literature. Early programmable calculators could do regressions. But what was behind this mysterious line through the points? This book explains it every way they can think of...there are so many ways they apply the simple y=mx+b that once you can master the line in cartesian space the rest is easy. I took this book in the two semester suggested way with regression first, ANOVA second. I did very well with these course and got a stats degree. I am very fond of this book now. Yes, my data diskette was corrupted at one point and I did have to ask our professor to make me a copy. I also agree with another reviewer that the examples are good. These allowed me to learn about the application areas and remain critical of the models. However, I can see that this book could confuse some and it is very expensive. Also I agree that when solving the later problems in the book one has to flip back to the first occurance of the dataset but if your working on a computer this isn't really as much of a problem. But it is still a problem because you have to know what variable is what. A favorite school book? Yes it is.