Stimulate learning through discovery With its emphasis on the discovery method, this book allows readers to discover solutions on their own rather than simply copy answers or apply a formula by rote. Readers will quickly master and learn to apply statistical methods, such as bootstrap, decision trees, and permutations, to better characterize, report, test, and classify their research findings. In addition to traditional methods, specialized methods are covered, allowing readers to select and apply the most effective method for their research, including: Tests and estimation procedures for one, two, and multiple samples Model building Multivariate analysis Complex experimental design
Throughout the text, the R programming language is used to illustrate new concepts and assist readers in completing exercises. Readers may download the freely available R programming language from the Internet or take advantage of the menu-driven S-PLUS(R) program.
Written in an informal, highly accessible style, this text is an excellent guide to descriptive statistics, estimation, testing hypotheses, and model building. All the pedagogical tools needed to facilitate quick learning are provided: More than two hundred exercises scattered throughout the text stimulate readers' thinking and actively engage them in applying their newfound skills Companion FTP site provides access to all data sets and programs discussed in the text Dozens of thought-provoking questions in the final chapter, Problem Solving, assist readers in applying statistics to address real-life problems Instructor's manual provides answers to exercises Helpful appendices include an introduction to S-PLUS(R) features
This textserves as an excellent introduction to statistics for students in all disciplines. The accessible style and focus on real-life problem solving are perfectly suited for both students and practitioners.
Introduction to Statistics Through Resampling Methods and R/S-PLUS(r) aspires to introduce statistical methodology to a wide audience, simply, intuitively, and efficiently, through resampling from data at hand and by way of the computer programs R and S-PLUS. The objective of the book is to use quantitative methods to characterize, review, report on, test, estimate, and classify findings.
Features include:
* The R and S-PLUS? programming are used to illustrate the concepts and to aid the reader in completing the exercises. R may be downloaded, without charge, for use under Windows, UNIX, or the Macintosh.
* One hundred or more exercises included in each chapter plus dozens of thought-provoking questions serve the needs of both classroom and self-study. The discovery method is utilized as often as possible, thereby forcing the conscientious reader to think her or his way to a solution rather than copy the answer or apply a formula straight out of the text
* Chatty, informal, sometimes humorous writing style allows greater access to a variety of reader backgrounds and interests
* Covers unusual topics such as tests and estimation procedures for one, two, and many samples; correlation; multivariable analysis; and complex experimental designs
* Provides a web site free of charge to all end-users that includes all data sets and programs in the text
