Editorial Review:
Applied Linear Statistical Models 5e is the long established leading authoritative text and reference on statistical modeling, analysis of variance, and the design of experiments. For students in most any discipline where statistical analysis or interpretation is used, ALSM serves as the standard work. The text proceeds through linear and nonlinear regression and modeling for the first half, and through ANOVA and Experimental Design in the second half. All topics are presented in a precise and clear style supported with solved examples, numbered formulae, graphic illustrations, and "Comments" to provide depth and statistical accuracy and precision. Applications used within the text and the hallmark problems, exercises, projects, and case studies are drawn from virtually all disciplines and fields providing motivation for students in virtually any college. The Fifth edition provides an increased use of computing and graphical analysis throughout, without sacrificing concepts or rigor. In general, the 5e uses larger data sets in examples and exercises, and the use of automated software without loss of understanding.
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Customer Reviews
Average Customer Rating: 
Excellent 2008-08-02
I was looking for a book that deals with details about regression analysis, simple or multiple, and advanced topics of statistical. I found it!
I recommend to buy this book. It's a excellent book!
Applied Linear Statistical Models
Only stats book I've read cover to cover 2008-02-22
As a PhD student in management, I found this book (5th edition) to have the perfect balance between clarity and rigor. If you are looking for a book that covers both the theory and application of linear regression methods, this is a terrific reference. It is not a light book, in content or weight, so be prepared to work through it slowly. I agree with other reviewers that it is not a book you can jump into without either an intro to stats or a good professor to take you through it (and I had both, which helped a lot). However, the time spent reading this book is well worth it. I read it cover to cover...a first for any math book I've owned (and I've had a lot of math classes...but none of it ever stuck). The chapters present information in layers, and if you still want more detail (for you PhD students in stats and finance), the footnotes are excellent. I bought it as a course text, but I have returned to this book many times. (You will get more out of the book if you are familiar with a little bit of basic matrix algebra.)
The Best Text 2007-11-30
This textbook is excellent. The examples are clear and are very valuable in connecting the theory to reality. The assigned text for my regression course, Introduction to Regression Modeling by Abraham and Ledolter, is horrible. I ended up using the Neter text to learn the material for the regression course as well as the design of experiment course for which it was assigned. I know I will keep the Applied Statistical Models forever as a highly valuable reference book. I highly recommend it.
Outstanding Non-Theoretic Linear Models Book, HUGE 2007-07-15
Second year Ph.D. student in Statistics at Iowa State University
I can't think of a single better non-theoretic linear models book. You need to have at least one semester of undergraduate statistics under your belt to follow this book, but it's useful and readable for everyone else. Undergraduates, graduates, professionals...whoever. Given its non-theoretic approach and extremely clear explanations, it can be read by undergraduates with only a minimal background in statistics, but it is comprehensive enough to be useful to anyone. There is no better linear models reference. The textbook is thick (almost 1400 pages) and covers most linear models topics in great detail including regression, ANOVA, and analysis of covariance. My only disappointment regarding content was the rather slim coverage of random and mixed effects models and GLM's. On a positive note, the book provides excellent coverage of diagnostics and remedial measures, which is very often skimmed over in linear models books. Additionally, it has exceptionally well-written, though fairly brief, coverage of model selection and validation, another topic that is a little lacking in many linear models books.
The explanations and choice of exercises are both well-done. The explanations and examples are both clear and thorough, although I would have definitely preferred to see more graphs. It's the kind of topic where visual illustration greatly increases understanding. Generally, the exercises seem a little bit too easy, especially for graduate students, but they do mix in a few harder problems and they pick good, non-contrived problems.
Whether you want a linear models book for learning purposes or if you just want a reference, this book is an excellent choice.
Emminetly Readable  2007-04-07
This book was a required text for my Data Analysis course. I am not a stats person and have had only a rudimentary introduction to the subject, so I was surprised to find that this is a very approachable book. It is A TOME, but only because the authors are so thorough in their explanations. If you have seen hypothesis testing and are comfortable with the normal distribution, you will be able to face this book. If you are not, be aware that the exercises in the first chapter refer to the prerequisite material not covered by the book.
After the introductory chapter, the authors gave just the right amount of theory to explain the topic at hand and give extensive footnotes for further information. Lots of graphs and example software output are included, all very helpful. I found the text to be well-organized, with coverage given to explanation and examples of each topic.
My one complaint with the book is that it included no instruction on how to work with software programs to get the desired results, so if you are entirely new to the area and do not know how to use Statistix (which has a thorough and self-explanatory help system), R, Minitab, and SAS (which do not), going will be rough. One of the other reviewers mentioned a SAS guide. You may need it if your professor does not demonstrate software use in class.
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