Bücher der Reihe Springer Texts in Statistics

Taken literally, the title "All of Statistics" is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. Statistics, data mining, and machine learning are all concerned with collecting and analysing data.

An ideal text for applied statisticians needing a standalone introduction to computational Bayesian statistics, this work by a renowned authority on the subject focuses on standard models backed up by real datasets. It includes an inclusive R (CRAN) package.

A carefully written text, suitable as an introductory course for second or third year students. The main scope of the text guides students towards a critical understanding and handling of data sets together with the ensuing testing of hypotheses.

This book is in two volumes, and is intended as a text for introductory courses in probability and statistics at the second or third year university level. The likelihood ratio statistic is used to unify the material on testing, and connect it with earlier material on estimation.

This graduate textbook covers topics in statistical theory essential for graduate students preparing for work on a Ph.D. degree in statistics. This new edition has been revised and updated and in this fourth printing, errors have been ironed out.

This graduate-level textbook, now in paperback, presents an introduction to Bayesian statistics and decision theory. Its scope covers both the basic ideas of statistical theory and some modern and advanced topics of Bayesian statistics.

A presentation of statistical methods that features use of graphical displays for exploring data and for displaying the analysis. It demonstrates how to analyze data - showing code, graphics, and accompanying computer listings.

We have sold 4300 copies worldwide of the first edition (1999). This new edition contains five completely new chapters covering new developments.

In its revised new edition, this book covers Markov chains in discrete and continuous time, Poisson processes, renewal processes, martingales and mathematical finance. Offers many examples and more than 300 carefully chosen exercises for better understanding.

This textbook offers an accessible and comprehensive overview of statistical estimation and inference that reflects current trends in statistical research.

This textbook presents an introduction to generalized linear models, complete with real-world data sets and practice problems, making it applicable for both beginning and advanced students of applied statistics.

The aim of this textbook (previously titled SAS for Data Analytics) is to teach the use of SAS for statistical analysis of data for advanced undergraduate and graduate students in statistics, data science, and disciplines involving analyzing data.The book begins with an introduction beyond the basics of SAS, illustrated with non-trivial, real-world, worked examples. It proceeds to SAS programming and applications, SAS graphics, statistical analysis of regression models, analysis of variance models, analysis of variance with random and mixed effects models, and then takes the discussion beyond regression and analysis of variance to conclude.Pedagogically, the authors introduce theory and methodological basis topic by topic, present a problem as an application, followed by a SAS analysis of the data provided and a discussion of results. The text focuses on applied statistical problems and methods. Key features include: end of chapter exercises, downloadable SAS code and data sets, and advanced material suitable for a second course in applied statistics with every method explained using SAS analysis to illustrate a real-world problem.New to this edition:•    Covers SAS v9.2 and incorporates new commands•    Uses SAS ODS (output delivery system) for reproduction of tables and graphics output•    Presents new commands needed to produce ODS output•    All chapters rewritten for clarity•    New  and updated examples throughout•    All SAS outputs are new and updated, including graphics•    More exercises and problems•    Completely new chapter on analysis of nonlinear and generalized linear models•    Completely new appendixMervyn G. Marasinghe, PhD, is Associate Professor Emeritus of Statistics at Iowa State University, where he has taught courses in statistical methods and statistical computing.Kenneth J. Koehler, PhD, is University Professor of Statistics at Iowa State University, where he teaches courses in statistical methodology at both graduate and undergraduate levels and primarily uses SAS to supplement his teaching.

It moves on to consider the various types of matrices encountered in statistics, such as projection matrices and positive definite matrices, and describes the special properties of those matrices.

As such, three course syllabi with expanded course outlines are now available for download on the book's page on the Springer website.A one-term course would cover material in the core chapters (1-4), supplemented by selections from one or more of the remaining chapters on statistical inference (Ch.

This book offers a step-by-step guide to the experimental planning process and the ensuing analysis of normally distributed data, emphasizing the practical considerations governing the design of an experiment. Experimental design is an essential part of investigation and discovery in science;

The material to be presented in the classroom constitutes a little more than half the text, while the rest of the text provides background, offers different routes that could be pursued in the classroom, as well as additional material that is appropriate for self-study.

Nonparametric methods, for instance, are often based on counts and ranks and are very easy to integrate into an introductory course. The ease of computation with advanced calculators and statistical software, both of which factor into this text, allows important techniques to be introduced earlier in the study of statistics.

This undergraduate statistical quality assurance textbook clearly shows with real projects, cases and data sets how statistical quality control tools are used in practice.  Among the topics covered is a practical evaluation of measurement effectiveness for both continuous and discrete data.  Gauge Reproducibility and Repeatability methodology (including confidence intervals for Repeatability, Reproducibility and the Gauge Capability Ratio) is thoroughly developed. Process capability indices and corresponding confidence intervals are also explained.  In addition to process monitoring techniques, experimental design and analysis for process improvement are carefully presented. Factorial and Fractional Factorial arrangements of treatments and Response Surface methods are covered.Integrated throughout the book are rich sets of examples and problems that help readers gain a better understanding of where and how to apply statistical quality control tools.  These large and realistic problem sets in combination with the streamlined approach of the text and extensive supporting material facilitate reader understanding.Second Edition ImprovementsExtensive coverage of measurement quality evaluation (in addition to ANOVA Gauge R&R methodologies)New end-of-section exercises and revised-end-of-chapter exercisesTwo full sets of slides, one with audio to assist student preparation outside-of-class and another appropriate for professors¿ lecturesSubstantial supporting materialSupporting MaterialSeven R programs that support variables and attributes control chart construction and analyses, Gauge R&R methods, analyses of Fractional Factorial studies,  Propagation of Error analyses and Response Surface analysesDocumentation for the R programsExcel data files associated with theend-of-chapter problem sets, most from real engineering settings

This book is aimed at the reader who wishes to gain a working knowledge of time series and forecasting methods as applied to economics, engineering and the natural and social sciences.

Since then, various drafts have been used at the University of Toronto for teaching a semester-Iong course to juniors, seniors and graduate students in a number of fields, including statistics, pharmacology, pharmacology, engineering, economics, forestry and the behav ioral seiences.

Die zweite Auflage der Thymuspathologie ist eine konzentrierte aktuelle Darstellung der vielfältigen Erkrankungen des Thymus, welche die modernen Methoden in das diagnostische Repertoire integriert und insbesondere auch klinische Fragestellungen berücksichtigt.'

Linear mixed-effects models (LMMs) are an important class of statistical models that can be used to analyze correlated data. It is built up incrementally, starting with a summary of the concepts underlying simpler classes of linear models like the classical regression model, and carrying them forward to LMMs.

This book is for students and researchers who have had a first year graduate level mathematical statistics course. Their research has been eclectic, often with a robustness angle, although Stefanski is also known for research concentrated on measurement error, including a co-authored book on non-linear measurement error models.

This is the first book at the graduate textbook level to discuss analyzing financial data with S-PLUS. master students in finance and MBA's, and to practitioners with financial data analysis concerns.

Two chapters on discrimination and classification, including logistic regression, form the core of the book, followed by methods of testing hypotheses developed from heuristic principles, likelihood ratio tests and permutation tests.

Presents the results of the modern theory of multivariate statistics to an audience of advanced students.

This textbook introduces the mathematical concepts and methods that underlie statistics. The book is committed to both a high level of mathematical seriousness and to an intimate connection with application. In its teaching style, the book is * mathematically complete * concrete * constructive * active.

This book is a contemporary introduction to theory, methods and computation in Bayesian Analysis. It focuses on topics that have stood the test of time and on emerging areas. No other such book is available in the market.

This book has been developed for a one-semester course usually attended by students in statistics, economics, business, engineering, and quantitative social sciences.

This book provides a broad overview of the basic theory and methods of applied multivariate analysis. The presentation integrates both theory and practice including both the analysis of formal linear multivariate models and exploratory data analysis techniques.