Graphics for Statistics and Data Analysis with R:2. Auflage Kevin J. Keen
Mathematica Navigator, Mathematics, Statistics and Graphics:Computer science, Software engineering CTI Reviews
The second edition of Statistics for Experimenters focuses on applications in the physical, engineering, biological, and social sciences. From the beginning, the book´s source of ideas is the scientific method itself and the need of the investigator to make his or her research as effective as possible through proper choice and conduct of experiments and appropriate analysis of data. After a problem is stated, appropriate statistical methods of design and analysis are discussed. And frequently, examples are presented for which standard mathematical assumptions are wrong, thus forcing the reader´s attention onto the essential precautions necessary in the conduct of the experiment to ensure valid conclusions. A Classic adapted to modern times Rewritten and updated, this new edition of Statistics for Experimenters adopts the same approaches as the landmark First Edition by teaching with examples, readily understood graphics, and the appropriate use of computers. Catalyzing innovation, problem solving, and discovery, the Second Edition provides experimenters with the scientific and statistical tools needed to maximize the knowledge gained from research data, illustrating how these tools may best be utilized during all stages of the investigative process. The authors´ practical approach starts with a problem that needs to be solved and then examines the appropriate statistical methods of design and analysis. Providing even greater accessibility for its users, the Second Edition is thoroughly revised and updated to reflect the changes in techniques and technologies since the publication of the classic First Edition. Among the new topics included are: * Graphical Analysis of Variance * Computer Analysis of Complex Designs * Simplification by transformation * Hands-on experimentation using Response Service Methods * Further development of robust product and process design using split plot arrangements and minimization of error transmission * Introduction to Process Control, Forecasting and Time Series * Illustrations demonstrating how multi-response problems can be solved using the concepts of active and inert factor spaces and canonical spaces * Bayesian approaches to model selection and sequential experimentation An appendix featuring Quaquaversal quotes from a variety of sources including noted statisticians and scientists to famous philosophers is provided to illustrate key concepts and enliven the learning process. All the computations in the Second Edition can be done utilizing the statistical language R. Functions for displaying ANOVA and lamba plots, Bayesian screening, and model building are all included and R packages are available online. All theses topics can also be applied utilizing easy-to-use commercial software packages. Complete with applications covering the physical, engineering, biological, and social sciences, Statistics for Experimenters is designed for individuals who must use statistical approaches to conduct an experiment, but do not necessarily have formal training in statistics. Experimenters need only a basic understanding of mathematics to master all the statistical methods presented. This text is an essential reference for all researchers and is a highly recommended course book for undergraduate and graduate students.
S is a powerful environment for the statistical and graphical analysis of data. It provides the tools to implement many statistical ideas that have been made possible by the widespread availability of workstations having good graphics and computational capabilities. This book is a guide to using S environments to perform statistical analyses and provides both an introduction to the use of S and a course in modern statistical methods. Implementations of S are available commercially in S-PLUS(R) workstations and as the Open Source R for a wide range of computer systems. The aim of this book is to show how to use S as a powerful and graphical data analysis system. Readers are assumed to have a basic grounding in statistics, and so the book is intended for would-be users of S-PLUS or R and both students and researchers using statistics. Throughout, the emphasis is on presenting practical problems and full analyses of real data sets. Many of the methods discussed are state of the art approaches to topics such as linear, nonlinear and smooth regression models, tree-based methods, multivariate analysis, pattern recognition, survival analysis, time series and spatial statistics. Throughout modern techniques such as robust methods, non-parametric smoothing and bootstrapping are used where appropriate. This fourth edition is intended for users of S-PLUS 6.0 or R 1.5.0 or later. A substantial change from the third edition is updating for the current versions of S-PLUS and adding coverage of R. The introductory material has been rewritten to emphasis the import, export and manipulation of data. Increased computational power allows even more computer-intensive methods to be used, and methods such as GLMMs,
Six Sigma Statistics with Excel and Minitab, Second Edition shows how to create reports, run analyses, and interpret results using these two widely used statistical software tools. This practical guide provides the perfect toolbox of theory, illustrations, explanations, exercises, and case studies both in the book and on an affiliated website to show how to use Excel and Minitab in conjunction with Six Sigma for an ideal improvement package. It reviews the quality tools that require Excel and/or Minitab, including measurement system analysis, SPC, the Taguchi method, and process capability analysis. * Affiliated website contains all 75 Excel/Minitab examples from book, plus at least 25 extras that aren´t included in the print version * Written by a Six Sigma Master Black Belt known for his expertise with statistics * Includes detailed graphics and real-world examples that can be applied to any industry
Agricultural Graphics:A Report of Exhibits Illustrating Agricultural Statistics at the World´s Industrial and Cotton Exposition at New Orleans, La (Classic Reprint) J. R. Dodge
Visual Statistics:Seeing Data with Dynamic Interactive Graphics Forrest W. Young, Pedro M. Valero-Mora, Michael Friendly
Data Analysis and Graphics Using R, An Example-Based Approach:Statistics, Statistics CTI Reviews