A nice review of MLE-based methods for categorical, limited, and ordinal dependent variables. Most social science data is best thought of as categorical, ordinal, etc., not interval, and so a readable treatment of one approach to the analysis of such data that does not rely on intervality assumptions is worthwhile.

The author has a very clear explanation of topics such as how MLE works, some numerical methods for maximizing, various tests associated with MLEs, etc., all written at a intermediate level. It's not too advanced so readers won't be driven off but also isn't a cookbook. Lots of nice examples throughout.

It's definitely in standard regression mode, which is not to say bad, just limited. It doesn't cover (or indeed discuss) topics such as categorical multivariate analysis, alternate loss functions for estimating categorical or ordinal regressions, including alternating least squares approaches or quantile regression, categorical or ordinal time series, or instrumental variables.... It's not the last word on the topic, but is certainly a solid first word.

Since I do statistical modeling in industry, I was looking for a good book on Logistic regression that would give me a deep understanding of the subject; one that also had wide coverage (Poison regression, Tobit models, ..etc.). I decided on J. Scott Long's book, after considering Applied Logistic Regression by Hosmer and Lemeshow, and Limited Dependent and Qualitative Variables in Econometrics by Maddala. I must say I am very pleased with my choice. The topics are very clear, and the math is used as an aid to understanding, and you don't get bogged down in the math. It is a pleasure to read the book.

This book is especially useful to start understanding topics like ordered probit, multinomial logit, negative binomial regression and zero-inflated count models. Although it starts with a chapter on the linear regression model, it should not be mistaken for an introductory text. I would certainly advise readers with limited background in regression models to start with other books, like the one of Wooldridge (Introductory Econometrics). The quality of this book must be that I've yet to see a book that explains these topics more intuitively. That is not to say it is easy or without mathematics, it's not. It just looks like the mathematics is only used for better comprehension, not to give you the full proof. Furthermore, while reading it you get the feeling that the author understands what you, as a researcher, are interested in. This allows him to focus on the topics of interest, like model selection and testing and interpretation of output. So although this is not a cookbook, it may well be the closest thing to it, especially in combination with his new book on applying these models in Stata. It is a pity that the author stops short of non-parametric models (next edition?).

I had to use a book written by the professor teaching me probability. His book being very poor quality I decided to try this one instead. With success, it is a very clear and compact book. The only thing I found is that it does not cover enough distributions, and that there are quite a few typos.

I purchased around $200 worth of textbooks on Probability and Random Processes for a graduate level course I took. Of those books, this one got the most use. It summed up topics concisely and provided adequate examples with enough detail to actually show me how to get an answer. If you are getting regular textbooks on this topic, it would be well worth your while to pay the extra small price for this handy guide. It will be well worth the money.

After being out of school for ten years, this book proved to be essential for my M.S.E.E. course in Random Signals. This book concisely summed my undergraduate engineering Signals class with solved problems. In addition, the solved problems acted as a guideline for my Master's class. I felt that I could not have gotten through my class without this book.

The reason why I ordered this book in the first place was the advice of my professor. My professor is a Ph.D. in the telecommunications workplace, acting as an adjunct professor. This working Ph.D. continues to use his well-worn Schaum's as a reference.

Hey what can I say about this book? A great book. The quality is in the name Schums's.

My battered old copy of Spiegel has been a great help to me over the years. Dr. Spiegel provides comprehensive coverage of his topics (for example, unlike other authors of introductory texts he actually provides formulas for assessing skew and kurtosis) and he explains them simply and clearly. I have found his book particularly valuable when I have to use analytical designs or procedures which I do not normally use -- he provides a good overview and gives you a good idea of where you should be looking for further information. Now I've discovered there's an expanded second edition I'm going to order it.

I have used this book as a reference for understanding and solving various types of statistics problems for over ten years. It makes application of statistics concepts to real world problems easy because it contains many good example problems that are solved for the reader. One statistics teacher complemented me on my use of the book because it is very difficult to use the information in other textbooks on problems because they contain very few solved examples. I own a few other statistics books but this one is always the most useful.

This book is about common sense: the way ordinary people have reasoned about the world since the beginning of recorded history. It is a compendium of anecdotes, about anecdotal thinking. I find the insights engrossing, entertaining, and scholarly-if not scientific. This book hopes to rigorously analyze the processes that thinkers have followed throughout recorded history in order to reach rational conclusions. These processes are interesting in their history of use as official rules of thumb, but they are fatally flawed. The fundamental flaw is that the nonscientific processes are not reasoning- they are persuasion, as in rhetoric. Being nonscientific in nature, "The Science of Conjecture" is misnamed, but quite worthwhile to those of us who would like to understand the lawyer and jurors' mind.

This is the sort of book whose indispensability creeps up on you: you start it without any idea that you'll require it to broaden your mind, but it insidiously works its magic. Totally unclassifiable -- it mixes the disciplines of history, mathematics, philosophy and jurisprudence -- it also happens to be a rivettingly lucid read, notwithstanding the outwardly abstruse nature of its materials.

If you read "Sophies World" by Jostein Gaardner and wanted something with more bite, this book is it. It's one of the few truly intellectual books I've read without being academic or boring. I had no idea how much we take certain things in our 21st century thinking for granted. One example is juries and innocence until proven guilty. The book is a marvelous history of legal and ethical thinking and how we came to civilized methods to deal with charges of guilt. It makes me aware of the manipulative power of different styles of logical arguments. Buts it's not only about law. The author explains why Islam is fundamental (God can't be wrong) so why bother considering pros and cons of situations. Christianity was lucky to have the reformation and counter-reformation to challenge why there is probability/chance or unknowing. There are great sections on scientific theory - reasoning for hard sciences like physics and astronomy. Why cannot astrology be a science? Because there are no hard rules; too much depends on the art or experience of interpreters who "explain" exceptions to rules, because so many situations don't follow their rules. The sections on soft science describe biology and medicine, and the impact of clinical trials. How did we arrive at "scientific thinking" to establish proofs? Its all here. I'm not into mathematics and the title sounded so boring to me - mathematics and before the 16th century ie Pascal. If ever there was a case for "don't judge a book by its cover" this is it. Its solid reading, but it is also deeply satisfying and fascinating in understanding a little bit more about how and why we think like do in the 21st century. As an aside the author is also a Latin scholar who translates many texts, correcting false interpretations. But he does it in subtle ways; nothing show-off. James Franklin dazzles us with his humility one moment and superb, accessible writing on complicated subjects the next moment. I never knew that "like" and "probably" were introduced from Greek. Medieval Europeans did not have sophisticated languages that included "like" or "probably" but with medieval enlightment they were introduced. What an impact these two words had. The author corrects cultural misthinking of how poor medieval thinking was. It was an explosion of brilliance in virtually a person's lifetime from 1150-1200. The Renaissance was mild in comparison. This book touches and explains our human development of consciousness and thinking in so many fields eg law, medicine, science, ethics. The author draws on Ancient Greek texts, Roman texts, the Talmud, Jewish philosophers, Islamic philosophers, Christian theologians and even Sanskrit writings. The subjects discussed heavily affect my daily life and thinking. Understanding a little bit of what we take for granted, makes me reconsider glib, slick arguments I'm confronted with in newspapers and television every day. If you buy the book, it's a great read over 1-2 months that can be savored and sipped like a great wine.

This book has a home spun, coversational style of wtriting. It includes a range of six sigma implementation ideas, descriptions of quality methods and tools and their historical origons, and a wealth of reference sources.

The Table of Contents is sufficiently detailed to serve as a reference.

I enjoyed the read.

Dr. Stamatis has captured what six sigma is and how it needs to be implemented to be successful. He even explains the PDCA cycle that so many have heard about but have never had it explained before. He also covers some of the frequently asked questions in this endeavor and covers how teams should be involved to ensure success.

I highly recommend this reference.

I am a recent Graduate of Engineering and found this book to be an excellent introduction to the foundations of six sigma and problem solving. Dr. Stamtis goes beyond slogans and mission statements and describes the mindset an organization has to be in order to be successful. His book has helped me better understand the six sigma methodology and how it relates to the corporate environment in which I work.

In this book, Dr. Stamatis discusses, in easy to understand English, the organizational structure for the six-sigma implementation, and quality methods used in six sigma. This book is a must for all Engineers who are interested in understanding the fundamentals of six sigma.

I thoroughly enjoyed and the read, and plan on reading the entire series.

Hi Friends, I was introduced to SPSS package during my MBA in Agri-business at MANAGE Institute-Hyderabad, India.And I was amazed with its capabilities. It can do wonders for any organization. And the book which I'm writing about just helps you to master necessary skills. But before you open the book or SPSS software, I strongly recommend you to brush up with concepts of Statistics and more importantly which tool is best for analysis. This avoids confusion in interpreting the output.

My suggestion to SPSS Inc., would be to include student version of SPSS Base free of cost to increase it's popularity. This software is not used efficiently by several organisations. The real power of SPSS lies in Syntax reference. So next time you open SPSS be prepared to explore as much as you can. wirte your queries and opinions to me at gopinathkoneti@yahoo.com

This is the required text for an SPSS class I am taking. The book is well written and clear. It has proven to be a helpful and useful support text to what we cover in class, especially to go deeper on certain topics. If you want to (have to) learn SPSS, I recomment this book. Take courage, SPSS is not that hard and I am even finding myself enjoying it (thanks in part to this book)!

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This is one of the three best introductory books on SPSS that I am familiar with. The others are Data Analysis by Jeremy Foster and Discovering Statistics by Andy Field. SPSS is a powerful statistical tool and an exceptionally fine software package, its customer support is the best I have ever dealt with. Unfortunately their user manuals are not user friendly. SPSS 10.0 Guide to Data Analysis fills a gap. Norusis's book is written in a light, humerous style but addresses complex statistical concepts in an easy to understand manner. Statistics and SPSS are an arcane and diffcult subject, somehow Norusis has made her subject friendly and funny. This is not to say this is an easy book. One of the things I look for in software manuals is exercises that demonstrate the concepts of the software. The book includes a disk. There are 23 chapters in the book and exercises for 21 of those chapters. I have also seen this book on the book shelves of some of SPSS's training staff. I think that speaks highly of the quality of this book.

Darren George and Paul Mallery made a very good, exact book that really works as an introduction to SPSS. Those last versions For Windows of the SPSS software are excellent, sound and solid - and to write a book about that was not a very hard work. But the complexity of the program can be a disturbing burden to the beginner, believe me. The worst problem here is not related to the Windows commands explanations but with the lack of explanation of those intrincate OUTPUTS you get from some of the SPSS macros. Maybe in a future edition some attention will be given to that VERY important "detail". It would turn that good book in an excellent book !

After spending more than 5000 hours (along with Paul Mallery, my co-author) to write the book, I now take a few moments for a note of a more personal nature. First, the book listed here is the 2nd edition (SPSS version 9.0). the 3rd edition has recently been released (on SPSS 10.0), and Allyn & Bacon (the publisher) also has our book (the 1st edition) for SPSS version 7.5.

The most recent edition contains 28 chapters. The comment by the gentleman from Louisiana reflects the many e-mails we have received on this book from, literally, all over the world. Paul and I have gone to excruciating efforts, particularly on the first 16 chapters, to create a tool that makes SPSS absolutely clear to the beginner. While we routinely recommend that anyone who uses the book first take a course in statistics, for anyone with reasonable math aptitude, the first 16 chapters should be understandable without ANY statistical background. In the final 12 chapter our book explores more complex statistical procedures (log-linear models, factor analysis, discriminant analysis, multi-dimensional scaling, and others) in the identical format as the earlier chapters.

If you are looking for a book that is comprehensive yet ultimately understandable for fundamental statistical procedures (data entry, data manipulation, frequencies, descriptives, chi-squares, t-tests, correlations, ANOVAs, simple linear and multiple regression analysis, graphs) but includes excellent coverage on the more advanced procedures we suggest that this book was made for you.

We, the authors, welcome your comments. Many reader comments have been incorporated in new editions of the book.

Darren George

This book has proven to be both an excellent tutorial and reference for not only SPSS but also statistics in general. The step-by-steps are easy to follow--even for the a novice computer user. Each chapter also provides a brief summary of when that statistical proceedure is appropriate which is quite useful. In addition, each chapter's "Output" section tell you which of SPSS's abundant output is most important during interpretation. These "Output" sections also provide a brief list of defined term that are displayed on the SPSS output. The definition of these terms are quite easy to understand and will often include a clarification of values for that term. This is a reference manual I carry with me at all times and I strongly recommend it for anyone who has less than expert knowledge in either SPSS or data analysis in general.

This book has an excellent treatment of the various statistical methodologies. It has an easy-to-read treatment of basic probability with a gradual introduction into conditional prob. The coverage of mean, variance, weighted mean and the standard deviation is good. I was impressed with the explanation of the z statistic and hypothesis testing. The author had a good rendition of regression and correlation in single, as well as, multiple models. The style of this work is a belles lettres

treatment-almost akin to reading a story. Despite the simplicity, there is enough complexity for the quantitatively oriented readers. I would like to see an update of this book with some cross-referencing to the internet and more modern terminology. Despite the fact that it is an older work, there are few books which explain the material with the clarity of this text. Its weaknesses may be overcome by utilizing the REA Statistics or the Schaum's outline. This book is excellent for students who find math hard to comprehend. It serves all levels of students from the most advanced to the students requiring more time with the material.

I just wish he would write a new book and focus it around SPSS. Although the book was published long ago, it is written to those struggling in their Multivariate classes. It is very clear and concise. I could have read this in high school and understood every word. The book is not focused on linear algebra or higher level mathematics. Instead, he provides a clear conceptual understanding of the major techniques. If you read this much, buy the book and read it. I guarentee that in no time you will be asking your prof questions that will boggle his or her mind and you will score you some brownie points. Further, after reading it you will love the subject and study stats naked. Even if you decide not to keep this as a desk reference, you can always sell it to another student and use the cash to buy some beer. After reading this review, I only ask that you write Dr. Sam and tell him to keep writing. Tell him that we will revoke his Visa if he doesn't. I love you SAM. Peace to the world. Long live Dallas, TX! This book helped me earn an A+ in advanced stats in the MBA that I am enrolled in.

This volume is an excellent introduction for someone who needs a conceptual understanding of statistics without the rigor or depth of a specialist. The presention is very non-technical with each new subject carefully developed. Ideal for the general business or social science undergraduate or anyone needing a brief and relatively painless encounter with a vital subject.

Frank Harrell is a Professor who does a lot of consulting in medical research. This book covers a wide variety of topics in regression analysis including many advanced techniques including data reduction, smoothing techniques, variable selection, transformations, shrinkage methods, tree-based methods and resampling. But note the title "Regression Modeling Strategies". Unlike most advanced texts in regression this book emphasizes modeling strategies. So the focus is on things like variable selection and other techniques to avoid overfitting models and diagnostics to look for violations in assumptions such as variance homogeneity or normality and independence of residuals, or stability problems like colinearity.

The book covers an extensive collection of modern techniques for exploratory data analysis. Inferential methods are also considered and he deals appropriately with important issues (particularly for medical research) such as imputation of missing values. Many examples are considered and illustrated in S-PLUS.

Harrell also provides many rules of thumb based on his own experience building models. A lot of the techniques are illustrated using data from the Titanic where it is interesting to see which factors affected the probability of survival. My only disappointment was that there is perhaps too much emphasis on this one particular data set.

A standard regression text would be expected to include linear and nonlinear regression. Harrell goes much deeper including nonparametric regression, logistic regression and survival models (e.g. the Cox proportional hazards model).

This is clearly an advanced text that mathematicians and PhD students in statistics would find valuable. It is not for an engineer or novice statistican in industry (like myself) who has to come up with an accurate regression model with quantitative and qualitative data in a short period of time. My rating is four stars: buy this book only if you have the advanced statistical training to understand it, otherwise buy a simpler book if you want to get a basic understanding of the subject.

This text does a five star job of what the title advertises. The book could be used for a one year graduate course in applied linear models. The writing is excellent, and topics very up to date. This is for graduate students with a good foundation in mathematical statistics and applied statistics. Very good integration with modern statistical packages.

SPSS is the most user-friendly of the major statistical packages, and in general I'm a big fan, having taught graduate statistics with SPSS and being a regular user in my own research. The student version, however, has one very serious limitation not mentioned in the promotional materials: it won't let you write in programming code ("SPSS syntax"). Most of us don't want to program, anyway, but the full version of SPSS allows you to set up your analyses in dialog boxes (which are great), and then save those analyses to a syntax window. Then, if you ever want to repeat your analyses, you don't have to go through all the bother again - you just re-run your syntax code. While this may sound like a minor issue, it's not - for the student, it means that three hours of homework might have to be repeated from scratch if the computer freezes; for the professional, it means you can't replicate your analyses easily. SO . . . all in all, sincerely high marks for SPSS, but low marks for this version.

Its a very powerful books but the programs need changes as you all have and old computer cannot use this version. I think spss should think the old model or put the label outside the cds that this programs need a better computer or speed or new monitor.

This book explain in very good way how to use efficiently statistical techniques, stating from description of data, plotting and examining them in order to notmalize them and then help to build the Multiple Regration Model. I used SPSS in my research and it is a very helpfull tool. And I think this book would be interesting for post graduate students and teachers.