applied statistics using spss, statistica, matlab and r

ISBN-13: 9783540719717 / Angielski / Twarda / 2007 / 536 str.

Joaquim P. Marques de Sa

Opis

Four years have passed since the first edition of this book. During this time I have had the opportunity to apply it in classes obtaining feedback from students and inspiration for improvements. I have also benefited from many comments by users of the book. For the present second edition large parts of the book have undergone major revision, although the basic concept - concise but sufficiently rigorous mathematical treatment with emphasis on computer applications to real datasets -, has been retained. The second edition improvements are as follows: - Inclusion of R as an application tool. As a matter of fact, R is a free software product which has nowadays reached a high level of maturity and is being increasingly used by many people as a statistical analysis tool. - Chapter 3 has an added section on bootstrap estimation methods, which have gained a large popularity in practical applications. - A revised explanation and treatment of tree classifiers in Chapter 6 with the inclusion of the QUEST approach. - Several improvements of Chapter 7 (regression), namely: details concerning the meaning and computation of multiple and partial correlation coefficients, with examples; a more thorough treatment and exemplification of the ridge regression topic; more attention dedicated to model evaluation. - Inclusion in the book CD of additional MATLAB functions as well as a set of R functions. - Extra examples and exercises have been added in several chapters. - The bibliography has been revised and new references added.

Recenzje

From the reviews:

"Aimed at students, professionals and research workers who need to apply statistical analysis to a large variety of practical problems using SPSS, MATLAB and STATISTICA, this book provides a comprehensive coverage of the main statistical analysis topics ... . The relevant notions and methods are explained concisely, illustrated with practical examples using real data, presented with the distinct intention of clarifying sensible practical issues." (T. Postelnicu, Zentralblatt MATH, Vol. 1028, 2004)

From the reviews of the second edition:

"Readership: Students, professionals and research workers ... who are interested in statistical methods and statistical program packages. The book is a ... large treatment of many basic statistical methods and procedures. It presents both theoretical issues and a wide variety of applications, examples and exercises. These cover such areas as engineering, medicine, biology, psychology, economy, geology, and astronomy. ... After all, I would easily recommend this book for those who are interested in the program packages mentioned in the title." (Kimmo Vehkalahti, International Statistical Review, Vol. 75 (3), 2007)

Spis treści

Presenting and Summarising the Data.- Estimating Data Parameters.- Parametric Tests of Hypotheses.- Non-Parametric Tests of Hypotheses.- Statistical Classification.- Data Regression.- Data Structure Analysis.- Survival Analysis.- Directional Data.

Blurb

This successful textbook is intended for students, professionals and research workers who need to apply statistical analysis to a large variety of practical problems using SPSS, MATLAB, STATISTICA and R. The book provides a comprehensive coverage of the main statistical analysis topics important for practical applications such as data description, statistical inference, classification and regression, factor analysis, survival data and directional statistics. The relevant notions and methods are explained concisely, illustrated with practical examples using real data, presented with the distinct intention of clarifying sensible practical issues. The solutions presented in the examples are obtained with one of the software packages in a pedagogical way. It provides guidance on how to use SPSS, MATLAB, STATISTICA and R in statistical analysis applications without having to delve in the manuals.
Major improvements of the second edition are the inclusion of the R language as one of the application tools, a new section on bootstrap estimation methods, a revised explanation and treatment of tree classifiers as well as extra examples and exercises.