Introduction to Time Series Analysis and Forecasting (Wiley Series in Probability and Statistics) Review

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(More customer reviews)I waited and waited before purchasing books for my classes because I did not want to pay the bookstore price. Three days before class, I remembered to check out Amazon and they had the books and they were at least $50.00 cheaper. I also used two-day shipping and I had my books on the first day of class.

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An accessible introduction to the most current thinking in and practicality of forecasting techniques in the context of time-oriented data.
Analyzing time-oriented data and forecasting are among the most important problems that analysts face across many fields, ranging from finance and economics to production operations and the natural sciences. As a result, there is a widespread need for large groups of people in a variety of fields to understand the basic concepts of time series analysis and forecasting. Introduction to Time Series Analysis and Forecasting presents the time series analysis branch of applied statistics as the underlying methodology for developing practical forecasts, and it also bridges the gap between theory and practice by equipping readers with the tools needed to analyze time-oriented data and construct useful, short- to medium-term, statistically based forecasts.
Seven easy-to-follow chapters provide intuitive explanations and in-depth coverage of key forecasting topics, including:

Regression-based methods, heuristic smoothing methods, and general time series models

Basic statistical tools used in analyzing time series data

Metrics for evaluating forecast errors and methods for evaluating and tracking forecasting performance over time

Cross-section and time series regression data, least squares and maximum likelihood model fitting, model adequacy checking, prediction intervals, and weighted and generalized least squares

Exponential smoothing techniques for time series with polynomial components and seasonal data

Forecasting and prediction interval construction with a discussion on transfer function models as well as intervention modeling and analysis

Multivariate time series problems, ARCH and GARCH models, and combinations of forecasts

The ARIMA model approach with a discussion on how to identify and fit these models for non-seasonal and seasonal time series
The intricate role of computer software in successful time series analysis is acknowledged with the use of Minitab®, JMP®, and SAS® software applications, which illustrate how the methods are imple-mented in practice. An extensive FTP site is available for readers to obtain data sets, Microsoft Office PowerPoint® slides, and selected answers to problems in the book. Requiring only a basic working knowledge of statistics and complete with exercises at the end of each chapter as well as examples from a wide array of fields, Introduction to Time Series Analysis and Forecasting is an ideal text for forecasting and time series courses at the advanced undergraduate and beginning graduate levels. The book also serves as an indispensable reference for practitioners in business, economics, engineering, statistics, mathematics, and the social, environmental, and life sciences.

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Research Methods in Family Therapy, Second Edition Review

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(More customer reviews)This book is not written like a typical textbook. It is pretty easy to understand, and is not as dry as other books on research methods. As an MFT student I did not think that this book was a must-have, but it did help me with my thesis, and it helped me to better understand the methods sections of journal articles.

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Fully revised and updated, the second edition of this widely adopted text and professional reference reflects significant recent changes in the landscape of family therapy research. Leading contributors provide the current knowledge needed to design strong qualitative, quantitative, and mixed-method studies; analyze the resulting data; and translate findings into improved practices and programs. Following a consistent format, user-friendly chapters thoroughly describe the various methodologies and illustrate their applications with helpful concrete examples. Among the ten entirely new chapters in the second edition is an invaluable research primer for beginning graduate students. Other new chapters cover action and participatory research methods, computer-aided qualitative data analysis, feminist autoethnography, performance methodology, task analysis, cutting-edge statistical models, and more.

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Applied Regression Analysis (Wiley Series in Probability and Statistics) Review

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(More customer reviews)Draper and Smith have long had a reputation for an outstanding book on regression analysis written at an elementary to intermediate level. I have long had a copy on my bookshelf and continue to purchase the revisions. They are careful to keep the book current by always incorporating new advances. This edition includes many of the recent advances in regression diagnostics as well as a description of the bootstrap approach to regression problems. Those interested in regression graphics should consult the book by R. Dennis Cook. More on the bootstrap can be found in my book "Bootstrap Methods: A Practitioner's Guide" or the other fine books by Efron and Tibshirani, Davison and Hinkley, and Lunneborg.

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An outstanding introduction to the fundamentals of regression analysis-updated and expanded The methods of regression analysis are the most widely used statistical tools for discovering the relationships among variables. This classic text, with its emphasis on clear, thorough presentation of concepts and applications, offers a complete, easily accessible introduction to the fundamentals of regression analysis. Assuming only a basic knowledge of elementary statistics, Applied Regression Analysis, Third Edition focuses on the fitting and checking of both linear and nonlinear regression models, using small and large data sets, with pocket calculators or computers. This Third Edition features separate chapters on multicollinearity, generalized linear models, mixture ingredients, geometry of regression, robust regression, and resampling procedures. Extensive support materials include sets of carefully designed exercises with full or partial solutions and a series of true/false questions with answers. All data sets used in both the text and the exercises can be found on the companion disk at the back of the book. For analysts, researchers, and students in university, industrial, and government courses on regression, this text is an excellent introduction to the subject and an efficient means of learning how to use a valuable analytical tool. It will also prove an invaluable reference resource for applied scientists and statisticians.

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Intermediate Statistics: A Modern Approach Review

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(More customer reviews)This is yet another well written book by Stevens. It goes into a sufficient amount of detail to understand the how, when, why, and where of these statistical analyses. One does not have to be a statistician to understand it. This book has great utility. The one disappointment was the lack of updated SPSS syntax, but other than this, it is well worth every penny.

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James Stevens’ best-selling text, Intermediate Statistics,is written for those who use, rather than develop, statistical techniques. Dr. Stevens focuses on a conceptual understanding of the material rather than on proving the results. SAS and SPSS are an integral part of each chapter. Definitional formulas are used on small data sets to provide conceptual insight into what is being measured.The assumptions underlying each analysis are emphasized and the reader is shown how to test the critical assumptions using SPSS or SAS. Printouts with annotations from SAS or SPSS show how to process the data for each analysis. The annotations highlight what the numbers mean and how to interpret the results. Numerical, conceptual, and computer exercises enhance understanding. Answers are provided for half of the exercises.The book offers comprehensive coverage of one-way, power, and factorial analysis of variance, repeated measures analysis, simple and multiple regression, analysis of covariance, and HLM. Power analysis is an integral part of the book. A computer example of real data integrates many of the concepts. Highlights of the Third Edition include:A new chapter on hierarchical linear modeling using HLM6A CD containing all of the book’s data setsNew coverage of how to cross validate multiple regression results with SPSS and a new section on model selection (Chapter 6)More exercises in each chapter.Intended for intermediate statistics or statistics II courses taught in departments of psychology, education, business, and other social and behavioral sciences, a prerequisite of introductory statistics is required. An Instructor's Resource is available upon adoption. See www.researchmethodsarena.com .

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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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The Design and Analysis of Computer Experiments (Springer Series in Statistics) Review

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(More customer reviews)This is a description of core practice in computer experiments,
written by authorities in the field. After an introduction it divides
into three parts: (1) building a statistical model of the underlying
computer code, known as a surrogate or an emulator; (2) choosing at
which settings to evaluate the code, eg, for the purposes of building
an emulator or for optimisation; (3) inference and validation (a
single chapter). There is a brief Appendix containing basic
distributional information, and a more extensive Appendix describing
the PErK software for building an emulator.
This book is consistent in its level and presentation. It serves as
an introduction to the field, providing orientation and an overview of
the literature. It is moderately technical; a Masters Statistician
should be comfortable with the mathematics. Derivations, where they
are given, are thorough, and the key results are clearly (sometimes
exhaustively!) presented. The technical and practical material is
well-blended. The Ch 2 material on stochastic processes gives a good
example of this: the boundary between what needs to be known and what
can be taken as given is well-delineated, and references are given by
author and page.
Note, however, that this book does not claim to be a handbook to
performing computer experiments: as far as I know such a book does not
exist. There are technical issues which the book does not address but
which are important in practice. In particular, choice of regression
functions and empirical estimation of correlation lengths in the
residual process---as advocated by the authors---can be very tricky in
practice. For this reason, I would like to have seen material on
emulator diagnostics: leave-one-out, or one-step-ahead (prequential),
for example.
As a broader observation, the authors' treatment seems tuned mainly to
engineering applications. Many computer experiments concern
environmental applications, which introduce a number of additional
challenges. Issues of scale are often a practical problem: how to
deal with large input spaces, large output spaces, and long
model-evaluation times. The uncertain model-inputs, for example,
might include the initial value of the state vector and the forcing,
comprising thousands of quantities if we are dealing with a climate
model. This will affect both emulator construction and experimental
design (sequential experimental design becomes much more important).
For environmental models the issue of model-validation can be subtle,
requiring as it does our assessment of model-imperfections: these can
be the dominant source of uncertainty, unlike in many engineering
applications.
This is not to criticise the authors, whose book which is usually on
or near my desk. They have done an excellent job of describing the
core material in a rapidly-developing field.

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This book describes methods for designing and analyzing experiments conducted using computer code in lieu of a physical experiment. It discusses how to select the values of the factors at which to run the code (the design of the computer experiment). It also provides techniques for analyzing the resulting data so as to achieve these research goals.

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Data Mining Techniques in CRM: Inside Customer Segmentation Review

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(More customer reviews)This book is an excellent guide for the practitioner, a data mining specialist who needs to do something with his/her customer information. It covers basic machine learning algorithms like kmeans, decision trees and self organization maps, pca, etc. I recommend this book as a starting point for people who don't have any experience with data mining and statistics and they want to do something with their data. It is a big plus for the book that it gives hints about how to choose the options for algorithms, since this is very critical and hard to find. We as a company use this book to understand the problems that the business world faces. I think the book has the right size and sufficient number of examples that are very well explained.
Although I understand the need of picking a tool to express the examples I think sticking to SPSS so tightly gives a bias to the book of what is feasible and what is not. I think some examples with open source like R would help the book to be less biased. But again I don't think it is a major issue, since the majority of the audience is professionals that will buy SPSS.
My major objection though, has to do with the distinction of machine learning and statistical learning that authors make. As it is stated in page 61 decision trees and neural networks are machine learning methods and not statistical ones, while pca is a statistical one and not a machine learning one. In reality all of them are statistical methods, machine learning and statistical learning are the same thing in the literature. A more sensible taxonomy between statistical methods is parametric/non-parametric. It is true that in general parametric methods can be faster versus non-parametric, but the statement in the book that they are more accurate is not in general valid. Nonparametric methods are slower but they are by far the most accurate. In reality though, nonparametric methods have been accelerated recently and they can actually be as fast as parametric ones.
I definitely recommend this book and I think the authors have done very good job, it filled a gap between science and business.
Nikolaos Vasiloglou
CTO Analytics 1305

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