Stats: Modeling the World Review

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(More customer reviews)My study habits and high school curriculum (early 1950s) did not prepare me for college calculus, so I was limited to taking applied statistics courses as an undergraduate (6 in all), all before the days of computers. I have used statistics all during my professional career and constantly searched for new books to refresh my memory or to learn new statistical techniques. Intro Stats by Velleman and De Veaux is by far the best text I have ever encountered. It is written in an easily understandable style, is well structured, and very user friendly. I recommend it highly.

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Stats: Modeling the World is a modern book in many ways. It carries a core focus on statistical thinking throughout the text, emphasizing how statistics helps us to understand the world. And it utilizes both graphing calculator and computer software technologies in doing Statistics. The topic order is designed to ensure that each new topic fits into the growing structure of understanding that students build. The mantra of Think, Show, and Tell is repeated in every chapter, emphasizing the importance of thinking about a statistics question and reporting our findings. The authors know that the best way to teach is with humor. The book is fun to read. And students report that they actually read it. (Honest!)--This text refers to an out of print or unavailable edition of this title.

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Predictive Modeling with SAS Enterprise Miner: Practical Solutions for Business Applications Review

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(More customer reviews)Excellent book with detailed illustration of steps using Eminer. Great for users of Eminer with understanding of data mining methods.

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Predictive Modeling with SAS Enterprise Miner: Practical Solutions for Business Applications demonstrates how to make the fullest use of SAS Enterprise Miner software. Kattamuri Sarma provides an in-depth explanation of the methodology and the theory behind each tool that he covers, and then shows you how the software performs the tasks. Step by step, you'll be able to compare manual calculations with the calculations that are performed by SAS Enterprise Miner. Examples from the insurance and banking industries are based on simulated, but realistic, data. The approaches discussed in this book are relevant to any industry.
Here are a few of the topics discussed in detail:
data collection and data cleaning
data exploration
decision trees and regression trees
logistic regression models
neural networks
variable selection and variable transformation

You need this book if you are a graduate student interested in predictive modeling, an expert in data mining who is not familiar with SAS Enterprise Miner, or a business analyst who needs an introduction to predictive modeling using SAS Enterprise Miner. To get the most from this book, you should be familiar with elements of statistical inference and probability, simple algebra, ordinary least squares, logistic regression, and Base SAS software.

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Modeling Survival Data: Extending the Cox Model (Statistics for Biology and Health) Review

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(More customer reviews)Terry Therneau is a research statistician at the Mayo Clinic and Patricia Grambsch is a Professor of Biostatistics at the University of Minnesota. The Cox proportional hazards model has been one of the key methods for analyzing survival data with covariates for the last 25 years. Proportionality is a key assumption that limits its use. There has long been a need to find methods which diagnose when the hazard rates are not proportional and provide alternative methods in such situations. Using the theory of counting processes the authors are able to extend the Cox model to more general situations including multiple/correlated event data using either marginal models or random effects (frailty) models. Time dependent covariates are also covered. Some of the theory of martigales and counting processes is included to make the book self-contained. Generalized residuals are used to identify outlying and influential observations (analogous to ordinary regression) and also to assess the proportional hazards assumption.
Although the topics are advanced and the mathematical level is high the book is designed for practitioners, emphasizing applications and providing numerous examples, many from the authors' experience. Statistical analyses are done in SAS and SPlus. The authors tend to use SAS for data management and analysis and SPlus for diagnostics and other plots. Therneau is an expert programmer who has written much of the necessary software in both systems.
Therneau gave an excellent short course that I attended a couple of years ago at the Joint Statistical Meetings based on a draft of the text. The finished product is as good as I expected.
The appendices include SAS and S-Plus tutorials on survival analysis and provide SAS Macros and S functions to apply the new methodology.
The book is now (December 2008) in its 6th printing which is another testament to its value and popularity and a nice deal at amazon's current price of $87. But O'Quigley's book is out now too. So maybe Terry and Patricia should be thinking about doing a revision if they don't already have one in the works.


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This book is for statistical practitioners, particularly those who design and analyze studies for survival and event history data. Building on recent developments motivated by counting process and martingale theory, it shows the reader how to extend the Cox model to analyze multiple/correlated event data using marginal and random effects. The focus is on actual data examples, the analysis and interpretation of results, and computation. The book shows how these new methods can be implemented in SAS and S-Plus, including computer code, worked examples, and data sets.

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Bayesian Modeling Using WinBUGS (Wiley Series in Computational Statistics) Review

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(More customer reviews)I try to read at least a couple of statistics books every year and this was one of them for 2009. So far I have been really impressed. If you want a complete introduction to Bayesian statistics, then buy this book. You will find a balanced blend of theory, applications, and the use of the WinBUGS software package all under one roof. The book's treatment of models for count data is notable. Ntzoufras has a nice way of expressing himself that makes the reading move along. I would have no compunction at all about using this book to teach a M.S.-level course for statistics majors. If you are an ecologist, say, then you should probably have both a probability and mathematical statistics course under your belt to fully absorb all that is going on.

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A hands-on introduction to the principles of Bayesian modeling using WinBUGS
Bayesian Modeling Using WinBUGS provides an easily accessible introduction to the use of WinBUGS programming techniques in a variety of Bayesian modeling settings. The author provides an accessible treatment of the topic, offering readers a smooth introduction to the principles of Bayesian modeling with detailed guidance on the practical implementation of key principles.
The book begins with a basic introduction to Bayesian inference and the WinBUGS software and goes on to cover key topics, including:

Markov Chain Monte Carlo algorithms in Bayesian inference

Generalized linear models

Bayesian hierarchical models

Predictive distribution and model checking

Bayesian model and variable evaluation

Computational notes and screen captures illustrate the use of both WinBUGS as well as R software to apply the discussed techniques. Exercises at the end of each chapter allow readers to test their understanding of the presented concepts and all data sets and code are available on the book's related Web site.
Requiring only a working knowledge of probability theory and statistics, Bayesian Modeling Using WinBUGS serves as an excellent book for courses on Bayesian statistics at the upper-undergraduate and graduate levels. It is also a valuable reference for researchers and practitioners in the fields of statistics, actuarial science, medicine, and the social sciences who use WinBUGS in their everyday work.

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