Applied Statistics Using SPSS, STATISTICA, MATLAB and R


By Joaquim P. Marques de Sá
Publisher:   Springer
Number Of Pages:   506
Publication Date:   2007-07-20
Sales Rank:   448321
ISBN / ASIN:   3540719717
EAN:   9783540719717
Binding:   Hardcover
Manufacturer:   
Studio:   
ReviewFrom 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. … The accompanying CD-ROM includes several specific software tools for the topics described in this book … ." (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)


Product DescriptionThis practical reference provides a comprehensive introduction and tutorial on the main statistical analysis topics, demonstrating their solution with the most common software package. Intended for anyone needing to apply statistical analysis to a large variety of science and enigineering problems, the book explains and shows how to use SPSS, MATLAB, STATISTICA and R for analysis such as data description, statistical inference, classification and regression, factor analysis, survival data and directional statistics. It concisely explains key concepts and methods, illustrated by practical examples using real data, and includes a CD-ROM with software tools and data sets. Readers learn which software tools to apply and gain insights into the comparative capabilities of the primary software packages. Major improvements of the second edition are the inclusion of the R language, a new section on bootstrap estimation methods and an improved treatment of tree classifiers as well as extra examples and exercises.