Mathematica Navigator, Mathematics, Statistics and Graphics:Computer science, Software engineering CTI Reviews
Need to learn statistics for your job? Want help passing a statistics course? Statistics in a Nutshell is a clear and concise introduction and reference for anyone new to the subject. Thoroughly revised and expanded, this edition helps you gain a solid understanding of statistics without the numbing complexity of many college texts. Each chapter presents easy-to-follow descriptions, along with graphics, formulas, solved examples, and hands-on exercises. If you want to perform common statistical analyses and learn a wide range of techniques without getting in over your head, this is your book. * Learn basic concepts of measurement and probability theory, data management, and research design * Discover basic statistical procedures, including correlation, the t-test, the chi-square and Fisher?s exact tests, and techniques for analyzing nonparametric data * Learn advanced techniques based on the general linear model, including ANOVA, ANCOVA, multiple linear regression, and logistic regression * Use and interpret statistics for business and quality improvement, medical and public health, and education and psychology * Communicate with statistics and critique statistical information presented by others
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,
´´Discover what you can do with R! Introducing the R system, covering standard regression methods, then tackling more advanced topics, this book guides users through the practical, powerful tools that the R system provides. The emphasis is on hands-on analysis, graphical display, and interpretation of data. The many worked examples, from real-world research, are accompanied by commentary on what is done and why. The companion website has code and datasets, allowing readers to reproduce all analyses, alongwith solutions to selected exercises and updates. Assuming basic statistical knowledge and some experience with data analysis (but not R), the book is ideal for research scientists, final-year undergraduate or graduate-level students of applied statistics, and practising statisticians. It is both for learning and for reference. This third edition expands upon topics such as Bayesian inference for regression, errors in variables, generalized linear mixed models, and random forests´´--Provided by publisher.
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
The Grammar of Graphics:Statistics and Computing. 2nd edition. 2005 Leland Wilkinson, D. Wills, D. Rope, A. Norton, R. Dubbs
The Grammar of Graphics:Statistics and Computing. 2nd ed. 2005 Leland Wilkinson, D. Wills, D. Rope, A. Norton, R. Dubbs
Graphics of Large Datasets:Visualizing a Million Statistics and Computing. Auflage 2006 Antony Unwin, Martin Theus, Heike Hofmann