This is the most comprehensive and up-to-date treatment of the analysis of variance models I have seen since the publication of the book by Henry Scheffe (1959) which is somewhat out of date. The mathematical and statistical prerequisites of Sahai and Ageel book are just about right for students in substantive fields who wish to take this course after completing one/two semester course in statistical inference. The book is well written and user friendly and contains a large number of worked examples and exercises which have been very helpful to me in assigning homework to my students. The coverage of SAS, SPSS and BMDP packages for performing analysis greatly enhances the practical usefulness of the book beyond any measure. The inclusion of a large number of appendices as review material and up-to-date and complete bibliography including a comprehensive set of statistical tables have immensely increased the reference value of the text. The authors deserve my most sincere and heartiest congratulations. I have prepared a more extensive and complete cirtique of the book for my students which can be obtained by writing to me. Dr. M.M. Ojeda Vice President of the Academic Affairs, University of Veracruz, Xalapa, Mexico.

Fractals make headlines from time to time[--are they everywhere?], and and they make beautiful color pictures; but they are also part of a substantial mathematical theory, even one with an exciting mathematical history. This lovely book presents the subject elegantly in a way that it can be taught to students. It starts with the basics, then systematically, step by step, it builds up the central results. Great for the classrom, or for selfstudy! In view of the many applications to geometric analysis, to PDE, and to statistics, it is likely that fractal geometry will soon be a standard math course taught in many (more) math departments. Example: By now it is widely recognized that the selfsimilarity aspects of the wavelet algorithms are key to their sucess. Compared to other books at the same level, for example, by Falconer, 1990, and the equally attractive one from 1985[Falconer: The geometry of fractal sets], Kigami's book stresses the analysis of the Laplace operator. Falconer covers the theory more generally, and his books have a slightly more potential theoretic bent.

I had the great pleasure of learning from this book when it was still in manuscript form. For the first time in my long history as a student I found that I understood the text at first reading! The chapters generally present two approaches: the technical (scary!) and the commonsense -- with the latter truly written in a commonsensical fashion. In fact, it is so well written that it is possible to go over the material on one's own. The book is pricey, but it delivers on its promise.

Cuthbert Daniel had over thirty years of experience consulting on experimentation in a variety of industries in 1976 when the book was written. He learned statistics from many of the great statisticians who were his colleagues, including John Tukey, Stu Hunter, Fred Mosteller, Bill Cochran and Henry Scheffe. As he says, the book is concerned only with confirmatory industrial experimentation where statistical methods can play an important role. He provides sage advice on the steps in planning and choosing a design for a particular experimental situation. Topics included are the factorial designs, fractional factorials, incomplete factorials (including the popular Plackett-Burman designs), trend robust plans and nested designs including split-plot designs. There is no coverage of response surface designs or other exploratory designs.

This is a terrific book that is fun to read. The only drawback is that it predates many of the recent developments such as the robust parameter designs motivated by the work of Taguchi. For a thorough up-to-date treatment see Hamada and Wu (2000) "Experiments" and my Amazon review of it.

In 1997 Ramsay and Silverman wrote a standard text that stands out as a classic book teaching the functional approach. With this new book they take the approach of teaching by example. Each of chapters 2 -12 present a case study first. The one on childhood growth is standard fair but they also present unusual cases such as modeling a juggler and analyzing handwriting. In eachcase they show how special characteristics of the data are best portrayed by treating curves or functions as individual data points. The application is presented first. Then the authors show how as consultants they formuated a functional data approach to the problem. Sometimes transformations are applied first. In another case principal component curves are constructed. After each problem is presented with the approach taken, the authors go back and present a "what we have seen" section that reviews the key points and lessons learned from the examples.

This is a wonderful approach that makes the material very accessible to practitioners as well as statisticians and illustrates the variety of problems and that can be handle by this approach.

One of the great features of this book is that it is addressed to the empirical researcher and it really tells you how to conduct good data-analysis with longitudinal data. It doesn't push one particular piece of software, either, but uses a variety of different software packages. The book is really easy to read, and clearly explained -- and, there's so much in it!

This is a great book, it tells you in a straightforward way how to analyze your longitudinal data to answer questions of critical importance in the social sciences. It's not wedded to one particular piece of software, as many books on statistical topics are, but uses examples of real data and different software (HLM, MLwiN, SAS, Stata) to conduct the analyses. An absolute must for the researcher who collects longitudinal data.

This is undoubtedly one of the best texts written on the topic.

Almost everything you need to know about how to input and read software results from someone who has actually analyzed real ( not simplified class room problems.) Very comprehensive coverage of large field. Most important, Stevens gives an insider's guide on what to avoid, what to disbelieve, and what is valid in this thicket of overlapping techniques. Great intro and guide to use of Statistical Power analysis, which is the linch-pin to planning experiments and obtaining valid results. Marvelous, and usable tables not readily obtainable elswhere that actually solve complicated problems (e.g. intra-class correlation the implicit reduction of significance.) The tables allow you to get quick results so you can short-circuit your cryptic and overweight software for basic problems. My edition, especially the tables are nearly worn-out from overuse.

Este livro traz um capítulo de introdução ( que pode-se dizer que é uma revisão )Depois nos outros capítulas explica de forma excelentes os métodos de análise multivariada, como por exemplo o método de componentes principais, regressão lógica, etc. Ainda no conteudo dos capítulos tem uma demonstração matemática também muito boa e se você quiser mais ele traz alguns programas em SAS e/ou SPSS e a interpretação da saída destes programas.

I took Ken Lange's course while he was writing this book. It is an excellent book on applied probability and rather densely packed. The text assumes a background in basic probability theory but it is otherwise self-contained with clear and logical development of each topic. The examples motivate and explain the theoretical development.