Introduction to Bayesian Statistics

Statistics: A Simple Guide for Students is an accessible, humourous and easy introduction to statistics for social science students. In this refreshing book, experienced author and academic Neil

Statistics: A Simple Guide for Students is an accessible, humourous and easy introduction to statistics for social science students. In this refreshing book, experienced author and academic Neil

This book is an introduction to statistics for linguists using the open source programming language and environment R in order to conduct quantitative studies. The author explains statistical concepts

This up-to-date account of algebraic statistics and information geometry explores the emerging connections between the two disciplines, demonstrating how they can be used in design of experiments and how they benefit our understanding of statistical models, in particular, exponential models. This book presents a new way of approaching classical statistical problems and raises scientific questions that would never have been considered without the interaction of these two disciplines. Beginning with a brief introduction to each area, using simple illustrative examples, the book then proceeds with a collection of reviews and some new results written by leading researchers in their respective fields. Part III dwells in both classical and quantum information geometry, containing surveys of key results and new material. Finally, Part IV provides examples of the interplay between algebraic statistics and information geometry. Computer code and proofs are also available online, where key examp

What is a p-value Anyway? offers a fun introduction to the fundamental principles of statistics, presenting the essential concepts in thirty-four brief, enjoyable stories. Drawing on his experience as

This manual presents the guide-like introduction to statistical techniques particulary oriented on medicine. The exercises present in the book make possible the self-education based on the internation

An accessible and engaging introduction to the study of probability and statistics Utilizing entertaining real-world examples, A Probability and Statistics Companion provides aunique, interesting, an

This book gives an introduction to deconvolution problems in nonparametric statistics, e.g. density estimation based on contaminated data, errors-in-variables regression, and image reconstruction. Som

First (and Second) Steps in Statistics, Second Edition provides a clear and concise introduction to the main statistical procedures used in the psychological and social sciences. The rationale and pr

First (and Second) Steps in Statistics, Second Edition provides a clear and concise introduction to the main statistical procedures used in the psychological and social sciences. The rationale and pr

This best-selling introduction to statistical analysis in the social sciences provides the right balance of conceptual understanding and step-by-step computational techniques.

Quantitative Research Methods for Health Professionals: A Practical Interactive Course is a superb introduction to epidemiology, biostatistics, and research methodology for the whole health care

Quantitative Research Methods for Health Professionals: A Practical Interactive Course is a superb introduction to epidemiology, biostatistics, and research methodology for the whole health care

The best-selling book, Elementary Statistics in Social Research, 7/e, has been adapted to provide a broad and accessible introduction to statistics for criminal justice enthusiasts. This straightfor

The essential introduction to the theory and application of linear models—now in a valuable new edition Since most advanced statistical tools are generalizations of the linear model, it is neces-sary

A frequent feature of numerical partial and ordinary differential equations, numerical solutions of convolution-type integral equations, stationary retrogressive time series in statistics, minimal rea

The first all-inclusive introduction to modern statistical research methods in the natural resource sciences The use of Bayesian statistical analysis has become increasingly important to natural reso

In fields such as biology, medical sciences, sociology, and economics researchers often face the situation where the number of available observations, or the amount of available information, is sufficiently small that approximations based on the normal distribution may be unreliable. Theoretical work over the last quarter-century has led to new likelihood-based methods that lead to very accurate approximations in finite samples, but this work has had limited impact on statistical practice. This book illustrates by means of realistic examples and case studies how to use the new theory, and investigates how and when it makes a difference to the resulting inference. The treatment is oriented towards practice and comes with code in the R language (available from the web) which enables the methods to be applied in a range of situations of interest to practitioners. The analysis includes some comparisons of higher order likelihood inference with bootstrap or Bayesian methods.

This introduction to the terminology of medical statistics describes more than 1500 terms, all clearly explained, illustrated and defined in non-technical language, without any mathematical formulae.

From 'Abcissa' to 'Zygosity determination' - this accessible introduction to the terminology of medical statistics describes more than 1500 terms all clearly explained, illustrated and defined in non-technical language, without any mathematical formulae! With the majority of terms revised and updated and the addition of more than 100 brand new definitions, this new edition will enable medical students to quickly grasp the meaning of any of the statistical terms they encounter when reading the medical literature. Furthermore, annotated comments are used judiciously to warn the unwary of some of the common pitfalls that accompany some cherished biomedical statistical techniques. Wherever possible, the definitions are supplemented with a reference to further reading where the reader may gain a deeper insight, so whilst the definitions are easily disgestible, they also provide a stepping stone to a more sophisticated comprehension. Statistical terminology can be quite bewildering for clini

This innovative new introduction to Mathematical Statistics covers the important concept of estimation at a point much earlier (Chapter 2) than others on this subject. Applies mathematical statistic

Colin Dyer’s Research in Psychology provides a concise and accessible introduction to research methods and statistics in the field, presenting a clear focus on the practicalities of how to effectively

"Elementary Statistics in Social Research: The Essentials" is a concise, uncomplicated introduction to statistics for the social sciences. Written to be understandable to a broad range of readers, par

International agreements, such as those governing arms control or the environment, virtually always require some degree of verification of information, in order that compliance can be established. To ensure that the verification process can be regarded as efficient, effective and impartial, it is important to have a mathematical model of it. One can be derived by applying methods from statistics and the theory of non-cooperative games, developed in part by John Nash, who received a Nobel prize in 1994 for his work. The methods permit the development of rational verification strategies, as well as such fundamental concepts as guaranteed probability of detection, timeliness of inspections and the deterrence of illegal activity. In this 1996 book, the required theory is introduced gradually in the context of specific real-world examples. The only prerequisites are simple calculus and statistics, so the book should be accessible to a broad range of scientists and non-scientists, in industr

The quantitative analysis of biological sequence data is based on methods from statistics coupled with efficient algorithms from computer science. Algebra provides a framework for unifying many of the seemingly disparate techniques used by computational biologists. This book, first published in 2005, offers an introduction to this mathematical framework and describes tools from computational algebra for designing new algorithms for exact, accurate results. These algorithms can be applied to biological problems such as aligning genomes, finding genes and constructing phylogenies. The first part of this book consists of four chapters on the themes of Statistics, Computation, Algebra and Biology, offering speedy, self-contained introductions to the emerging field of algebraic statistics and its applications to genomics. In the second part, the four themes are combined and developed to tackle real problems in computational genomics. As the first book in the exciting and dynamic area, it wi

This is a thorough introduction to fundamental ideas in modern statistics for readers without a calculus background. The only book at this level to discuss modern concepts of hypothesis testing and es

The MINITAB lab manual is designed to be used with any Elementary Statistics textbook. The lessons are self-explanatory and explore topics that are covered in all elementary texts. The purpose in writ

An application-based introduction to the statistical analysis of spatially referenced health data Sparked by the growing interest in statistical methods for the analysis of spatially referenced data

This is a lively textbook providing a solid introduction to financial option valuation for undergraduate students armed with a working knowledge of a first year calculus. Written in a series of short chapters, its self-contained treatment gives equal weight to applied mathematics, stochastics and computational algorithms. No prior background in probability, statistics or numerical analysis is required. Detailed derivations of both the basic asset price model and the Black–Scholes equation are provided along with a presentation of appropriate computational techniques including binomial, finite differences and in particular, variance reduction techniques for the Monte Carlo method. Each chapter comes complete with accompanying stand-alone MATLAB code listing to illustrate a key idea. Furthermore, the author has made heavy use of figures and examples, and has included computations based on real stock market data.

Based on two lectures presented as part of The Stone Lectures in Economics series, Arnold Zellner describes the structural econometric time series analysis (SEMTSA) approach to statistical and econometric modeling. Developed by Zellner and Franz Palm, the SEMTSA approach produces an understanding of the relationship of univariate and multivariate time series forecasting models and dynamic, time series structural econometric models. As scientists and decision-makers in industry and government world-wide adopt the Bayesian approach to scientific inference, decision-making and forecasting, Zellner offers an in-depth analysis and appreciation of this important paradigm shift. Finally Zellner discusses the alternative approaches to model building and looks at how the use and development of the SEMTSA approach has led to the production of a Marshallian Macroeconomic Model that will prove valuable to many. Written by one of the foremost practitioners of econometrics, this book will have wide

Based on two lectures presented as part of The Stone Lectures in Economics series, Arnold Zellner describes the structural econometric time series analysis (SEMTSA) approach to statistical and econometric modeling. Developed by Zellner and Franz Palm, the SEMTSA approach produces an understanding of the relationship of univariate and multivariate time series forecasting models and dynamic, time series structural econometric models. As scientists and decision-makers in industry and government world-wide adopt the Bayesian approach to scientific inference, decision-making and forecasting, Zellner offers an in-depth analysis and appreciation of this important paradigm shift. Finally Zellner discusses the alternative approaches to model building and looks at how the use and development of the SEMTSA approach has led to the production of a Marshallian Macroeconomic Model that will prove valuable to many. Written by one of the foremost practitioners of econometrics, this book will have wide

The book aims to provide both comprehensive reviews of the classical methods and an introduction to new developments in medical statistics. The topics range from meta analysis, clinical trial design,

This book offers a straightforward, “nuts and bolts” , introduction to statistics. The explanations are clear and simple and minimize calculations where possible. A diverse range of applications and e

Statistical methods are essential tools for analysts, particularly those working in Quality Control Laboratories. This book provides a sound introduction to their use in analytical chemistry, without

This is an introductory statistics textbook for business and management students which uses the innovative approach of 'statistical thinking'. Statistics courses are essential for business students bu

Statistics in Musicology offers an introduction to statistical and mathematical methods developed for use in music analysis, music theory, and performance theory.This unique book explores concrete met

A guide to using S environments to perform statistical analyses providing both an introduction to the use of S and a course in modern statistical methods. The emphasis is on presenting practical probl

With Flash Review on your side, studying statistics is a snap! Designed by veteran educators, tested by real students for clarity and brevity, Flash Review for Introduction to Statistics uses crisp, t

This is the second edition of a coherent introduction to the subject of asymptotic statistics as it has developed over the past 50 years. It differs from the first edition in that it is now more 'read

"Essential Business Statistics provides a comprehensive introduction to the basic numerical techniques used in business. The book has been designed to allow for self-paced learning. Student self-relia