I make measurements frequently and this book is great for providing the background to analyze your data.

I took undergraduate level statistics and it never really gave the practical applied background in how to analyze data. It merely presented concepts and presumed you knew how and why to apply them. This book is very good at helping you to understand the how and why.

I have read a number of other statistics book in search of the practical applied information provided in this book and did not find it in the other books.

The writing is clear and consice. There is enough background provided for even those unexposed to statistics.

I have not tried the software. Most of the formulas are easy to apply and can be implemented in simple programs or spreadsheets in very little time.

In short, I recommend this book to anyone making measurements of any kind.

Robinson's second edition continues the late Bevington's tradition of clear and concise writing, making this book a priceless reference for scientists. Robinson has added discussions of modern problems such as resolving closely-spaced peaks in a spectrum. The new version also adds chapters on Monte Carlo techniques and maximum-likelihood analysis, both powerful tools for data analysis made possible by better computers.

The chapter structure has been modified considerably, so those who have grown comfortable with the first edition over the past decades may not be able to find things as easily. Other than that, most of the weaknesses are computer-related. Much has changed even since 1992.

Robinson added an appendix on graphical presentation. This sounds promising but is a pretty trivial discussion of when to use linear or logarithmic axes and the advantages of a historgram. Might be useful for a very young student, but these days playing with such things is easy in any graphing program.

Many of the computer code snippets have been removed. Most of them were only a few lines of code with lots of comment lines anyway. The codes that remain have been moved from the main text to a densely-packed appendix, which makes them more difficult to study while reading the text.

The codes themselves have been updated from old FORTRAN to a structured language, but I would have preferred C or FORTRAN 90 over the chosen PASCAL. The latter may be useful for undergraduate students, but I've never seen a PASCAL compiler in a working physics lab.

The included disk is a now-obsolete 5.25" floppy. I had to hunt for a machine that could read it and copy over to a 3.5" disc. The text claims repeatedly that the disc has both FORTRAN 77 and PASCAL routines on it, but my copy only has the PASCAL.

In the end, it's the textual content that is important, and this book is a fantastic basic discussion of data analysis and statistics for students and a great reference for the practicing scientist.

Bevington's first edition of this book dates to the late 60's when Fortran ruled the world. I was crushed when I lost my copy in the mid 70's and am delighted to find he's written a modern updated edition!

James Ramsey has written the ideal introductory statistics text for those with inquisitive minds. Ramsey's The Elements of Statistics with Applications to Economics and the Social Sciences presents an insightful, but accessible approach to the processes of statistical reasoning and problem solving. There are plenty of well-developed and realistic case studies that offer the reader straightforward explanations of the statistical reasoning used in setting up the problem solution. Ramsey does not just present statistics as facts and numbers; the why's and how's behind the use of specific statistical tools required in case studies and other examples are addressed in a straight forward and simple to understand manner. This contrasts most contemporary introductory statistics texts where it seems there is always an attempt to be the biggest encyclopedia of statistical examples. Ramsey's style of exposition offers the reader the depth and perspective required to facilitate both the current and future statistical requirements of the reader. In short, this is one of the few introductory texts that allow the reader to attain a stable grounding in the field of statistics on one hand, and on the other, will still be a useful reference throughout one's professional career. I highly recommend Ramsey's text as both a starter text and as a reference for those looking to clarify their fundamental statistical queries.

This book is a wonderful introductory statistics text. In contrast to the usual extensive approach in which students are exposed to a large cookbook of statistical procedures that end up being memorized 'for the exam,' this text operates on the intensive margin in a successful effort to provide the conscientious student a far deeper introduction to statistical reasoning and practice.

Each chapter contains a large set of exercises and the text comes with a simplified student version of S-Plus. Most of the computational work required for these exercises can be carried out through a menu-driven GUI interface. To help facilitate learning, many worked examples are also provided.

The mathematical requirements include a little beyond what a student should have upon entry into a first calculus course in an American university, i.e., little beyond basic algebra. An appendix explains all the mathematics used in the text.

I enthusiastically recommend this text!

The Elements of Statistics: with Applications to Economics and the Social Sciences by James B. Ramsey is an innovative and excellent undergraduate level text on the foundations and reasoning of statistics estimation and inference. This book is written for the curious student who is interested in understanding the basics of statistical analysis, the intuition behind statistical and information processing, and the process of decision making based on some data. Most importantly, in this book Ramsey takes the student through a fascinating voyage of discovery. In this voyage, Ramsey devotes significant effort to explaining what are the fundamental rules underlying most data analyses within the social and natural sciences. This is done without requiring much prior knowledge of calculus and with almost no formal mathematics. Ramsey accomplishes this task by building on a large number of real world examples, some of which he re-evaluates at the end of each chapter. By doing so, he allows the reader (student or researcher) to see the real value of the knowledge just acquired in the most recent chapter. That is, "what can I understand now about that specific problem that I could not understand before." In that way the student is going through an on-going learning process. A process that allows one to understand the data by recognizing what is observed and what is not observed, what is random and what is not random, what process may have generated the data, and what one can infer from the data.
To summarize, once Ramsey expresses his philosophy of approaching statistical analyses, he proceeds to teach statistics in a completely new and innovative way. First, unlike existing undergraduate textbooks, Ramsey teaches the students via a "discovery" approach where each step starts with a new set of questions and the students are guided toward discovering the relevant answer, given the information they have. Second, the text is easy to read and is full with real world examples taken from a large number of disciplines. Finally, the book is equipped with complete software (S-Plus) that provides the necessary tool for the students to practice and understand how to work with real data. This is an ideal undergraduate level textbook. It is a very useful statistical text for the open minded and advanced undergraduate student and provides the teacher with a perfect teaching tool. It is highly recommended.

Amos Golan
Research Professor

This text give a top to bottom account of model building via RSM. If you have a question, consult this book.

George Box and Norman Draper have written an outstanding text on model building and response surface methods. This is an excellent companion to Box's text with Stu Hunter and William Hunter on experimental designs "Statistics for Experimenters". Another excellent, more recent and detailed account of response surface methods can be found in the book by Montgomery and Myers.

I do a lot of experimental work for my research and this book is definetely a good investment. I rate it amongst the best in the subject

McLachlan and Basford (1988) and Titterington, Smith and Makov (1985) were the first well written texts summarizing the diverse lterature and mathematical problems that can be treated through mixture models. Geoff McLachlan is the author of four statistics texts namely (1)McLachlan and Basford (1988) "Mixture Models:Inference and Applications to Clustering", Marcel Dekker, (2) McLachlan (1992) "Discriminant Analysis and Statistical Pattern Recognition", Wiley (3) McLachlan and Krishnan (1997) "The EM Algorithm and Extensions" Wiley and (4) McLachlan and Peel (2000) "Finite Mixture Models" Wiley. These four books are all related to the interesting problems in pattern recognition and clustering. Mixture models and the EM algorithm are tools used to solve problems in clustering and pattern recognition.

In each of his books McLachlan has shown an ability to be clear, authoritative, scholarly and thorough. He provides broad coverage of each topic with detailed references. This book is no exception. As he point out in the preface, the literature on mixture models has expanded tremendously since the appearance of his 1988 monograph with Kaye Basford making an updated text very appropriate.

Almost 40% of the 800 references in the text have appeared since 1995. The recent advances covered in the text include identifiability problems with mixture models, the analysis (fitting of mixture models) for real data sets using the EM algorithm and its extensions, properties of maximum likelihood estimators, applicability of asymptotic theory, use of bootstrap methods to assess accuracy of estimates, implimentation of Bayesian approaches through Markov chain Monte Carlo methods and the use of hierarchical mixtures-of-expert models for nonlinear regression as competitors to the MARS and CART algorithms.

This is a great book. Chapter 1 provides a nice overview of the subject with a thorough historical treatment, nicely presented in Section 1.18. In addition to the fact that it covers all the recent advances one can think of. The book also deals with fast implementations of the EM algorithm for data mining and other approaches to modifying the EM algorithm to handle large data sets. There is also a wealth of interesting real problems worked out in detail. These problems come from many disciplines, including interesting medical problems related to diabetes and hemophilia, nuclear test ban data analysis, image processing and competing risk survival analysis. It also covers some interesting aspects of multivariate normal mixture models and their applications.

A wonderful text that functions as well as a reference as it does as an introduction to mixture models. I was surprised by the depth and breadth of the book, which manages to describe almost every mixture model imaginable and then some more, including forms of the models themselves, parameter estimation and fit. Relationships between different models are made clear, lending the text a coherence that isn't undercut by vague generalities. The authors are particularly good at addressing issues of particular importance in mixture modeling, such as fit and model selection. Material is suprisingly recent as well. Overall, a great text that is probably destined to become the standard reference on mixture models.

Mixture models have become a hot topic in statistics. After you read this book, you will know why.

"Finite Mixture models" have come a long way from classic finite mixture distribution as discused e.g. Titterington et al(1985). A small sample should almost surely entice your taste, with hot items such as hierarchical mixtures-of-experts models, mixtures of GLMs, mixture models for failure-time data, EM algorithms for large data sets, and hidden Markov models. The book gives a lucid overview of recent developments on mixture models since 1990 (the aim of this book in the first place). It expounds on the modern viewpoint that mixtures can be usefully exploited as a mechanism for building flexible statistical models for complex processes, e.g. nonparametric Bayesian models. Balanced attention is given to all three modern approaches to fitting mixture models which include speed-up EM, Bayesian, and stochastic simulation. The whole book is superbly written, and very entertaining---It's hard to put it down once started. It is very update with 45 pages of references and an appendix listing available softwares.

I'm a big fan of Prof. McLachlan's books; and I believe, this latest book of his with one of his student D. Peel, should add another masterpeiece to the long list of marvelous statistics books coming out of Australia and New Zealand...

This review refers to the first edition, which, aside from the dated computer programs used for analyses, discusses a variety of topics that are not typically covered in traditional regression texts. Especially valuable is chapter 9 which consists of a situation where using a combination of linear and nonlinear fits simultaneously, complete with both qualitative and quantitative data. A great extension past books like Draper and Smith and Myers and Montgomery.

This is a classic text on regression. I am only familiar with an earlier edition but I am sure the style the writing and the content has not changed significantly. This book gives good practical advice as to how to fit regression models and considers all the pitfalls that applied statisticians are faced with. Many of the issues that are raised today including problems of overfitting, multiple collinearity, outliers, diagnostic plots etc. were all considered by these applied statisticians some 30 years ago.

This book is a classic work in the field of data analysis which is often cited in more recent texts. In the present world of Windows, SAS, SPSS, S-Plus, etc., the pioneering work of Daniel and Wood on computer applications to data analysis looks somewhat dated. However, it will repay careful study by anyone who wants to do thorough analysis of real world data, especially manufacturing process data, because it includes topics, such as nested data sets, which are common in industrial data, but regrettably cannot be analyzed by most standard linear regression techniques as presented in more recent texts. It is a very valuable adjunct to books like Draper and Smith.

Nelder and Wedderburn wrote the seminal paper on generalized linear models in the 1970s. Since then John Nelder has pioneered the research and software development of the methods. This is the first of several excellent texts on generalized linear models. It illustrates how through the use of a link function many classical statistical models can be unified into one general form of model. This unification is helpful both theoretically and computationally. Various applications are presented in a clear manner.

The first edition is already a well-known text and reference, this expanded version is even better. Very comprehensive and very helpful.

This is an important book. It is a mature, deep introduction to generalized linear models.

General linear models extend multiple linear models to include cases in which the distribution of the dependent variable is part of the exponential family and the expected value of the dependent variable is a function of the linear predictor. Besides the normal (Gaussian) distribution, the binomial distribution, the Poisson distribution and the Gamma distribution, are just some of the exponential family members most frequently encountered in the scientific literature. Using appropriate functions to join the dependent variable to the linear predictor many classic models of applied statistics are included in the broad frame of generalized linear models: "logistic regression", log-linear models, Cox's proportional hazards models are just some of them.

Further extensions to the "base" family of generalized linear models, such as those based on the use of quasi-likelihood functions, and models in which both the expected value and the dispersion are function of a linear predictor, are well presented in the book.

Examples, and exercises, introduce many non-banal, useful, designs.

There are some minor drawbacks. Some more advanced topics might have been introduced more smoothly (i.e. conditional likelihood). Some other topics are better understood when you are already familiar with the specific object of study (i.e. Cox's proportional hazards models as a generalized linear model). The book does not provide software examples, nor is it related with any specific statistical package. However, the maturity of the reader to whom the book is addressed should be so high that translating the majority of the examples presented in the book in the "language" of a familiar statistical package should not be a problem.

How can you expect us to buy the book without providing a table of contents?!

How can you expect people to buy this book without table of contents?!

This book takes the reader through the basics of probability theory right in through sampling theory, estimation, hypothesis testing, and regression analysis, and at the same time puts both the Classical and Bayesian approaches side by side, so that the reader can see the strengths and weaknesses of both approaches. This book is an excellent for both advanced undergraduates and graduate students interested in applied empirical research.

This is a great book for the S-Plus novice. While it doesn't cover every topic it does explain a lot of basics in easy to understand English. The examples and explanations are very informative.

The book "An Introduction to S and S-Plus" by Phil Spector is an excellent text for anyone who wants to get things done with the S/S-Plus statistical package. The book covers the basics of loading data into the system, how to manipulate datasets using the various operators and functions of S-Plus, as well as how to make full use of the graphics subsystem. Advanced topics such as how to dynamically load object code into S are also covered. The best part of the book is that it offers very useful examples of how to do things. Unlike many other computer books, the examples aren't contrived expository devices that the author cooked up in a minute. I've found that, even after using S-Plus for many years, I come back to this book over and over again for guidance on how to get things done. I give the book my highest recommendation.

Clear, concise, and to the point. Dr. Spector teaches statistics courses at UC Berkeley, and it shows.

The book is very good but if there is a student solution manual to go with it that would be very helpful. The book has very many useful problems but with no solutions i'm never sure if what i did was right or wrong.

I am studying the book. However it is little bit difficult to solve the example without solution, so I wonder if the solution exist or not. If do, I would like to take the solution. let me know whether it's possible or not.

I am going to introduce this book as one of two text books for probability course. So I need a problem solution manual. I am wondering if your company distribute the manual. would you let me know how can I get one. I send this message through review because I don't know how to apply for that. Your co-operation in this regard is appreciated.

In contrast to their graduate text "Time Series: Theory and Methods" this book is more elementary and introductory and is pitched at the advanced undergraduate level requiring only calculus, elementary statistics and matrix algebra. It gives very good coverage to a wide variety of time series models and includes some nonstationary models. In this second edition the chapter on nonstationary models includes the latest coverage of ARCH and GARCH models presented in a way that I found very accessible.

Computations are done with ITSM and in this edition the ITSM 2000 version 7.0 edition is included on a CD so that students can reproduce the authors' calculations and run analyses of their own.

Another nice feature of the text that distinguishes it from other texts at this level is the introduction of multivariate time series, coverage of state space models, chaos and cointegration. Ideas are illustrated with examples. Important theory is discussed but is kept brief and theorems and proofs are not given to the extent of their other more theoretical text.

Very good introductory book to ARMA models. Full of real-life examples that provide some intuitive insight about the issues that may arise when modelling time series and forecasting. Requires some initial knowledge in statistics and algebra but if you're involved in time series modelling, it should be your first book.

Very good introductory book to ARMA models. Full of real-life examples that provide some intuitive insight about the issues that may arise when modelling time series and forecasting. Requires some initial knowledge in statistics and algebra but if you're involved in time series modelling, it should be your first book.