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This book offers solutions to all 284 exercises in Advanced R, Second Edition. All the solutions have been carefully documented and made to be as clear and accessible as possible. Working through the exercises and their solutions will give you a deeper understanding of a variety of programming challenges, many of which are relevant to everyday work. This will expand your set of tools on a technical and conceptual level. You will be able to transfer many of the specific programming schemes direct...

This book summarizes the results of various models under normal theory with a brief review of the literature. Statistical Inference for Models with Multivariate t-Distributed Errors: * Includes a wide array of applications for the analysis of multivariate observations * Emphasizes the development of linear statistical models with applications to engineering, the physical sciences, and mathematics * Contains an up-to-date bibliography featuring the latest trends and advances in the field to provi...

An Introduction to Measure-Theoretic Probability, Second Edition, employs a classical approach to teaching the basics of measure theoretic probability. This book provides in a concise, yet detailed way, the bulk of the probabilistic tools that a student working toward an advanced degree in statistics, probability and other related areas should be equipped with. This edition requires no prior knowledge of measure theory, covers all its topics in great detail, and includes one chapter on the ba...

Introduction to Robust Estimation and Hypothesis Testing focuses on the practical applications of modern, robust statistical methods. The increased accuracy and power of modern methods is remarkable compared tothe conventional approaches of the analysis of variance (ANOVA) and regression. Through a combination of theoretical developments, improved and more flexible statistical methods, and the power of the computer, it is now possible to address problems withstandard methods that seemed insurmou...

Regression analysis is a core component of the statistics classes required of millions of students. Regression analysis has numerous practical applications, such as predicting sales, finding risk factors for a disease, or anticipating demand for goods at a given price, but the math is challenging, and many struggle with the course. As with all titles in the best-selling Manga Guide series, The Manga Guide to Regression Analysis combines comics with real-world examples to teach readers how to use...

*Furnishes a thorough introduction and detailed information about the linear regression model, including how to understand and interpret its results, test assumptions, and adapt the model when assumptions are not satisfied.*Uses numerous graphs in R to illustrate the model's results, assumptions, and other features.*Does not assume a background in calculus or linear algebra; rather, an introductory statistics course and familiarity with elementary algebra are sufficient.*Provides many examples u...

This textbook has been developed from the lecture notes for a one-semester course on stochastic modelling. It reviews the basics of probability theory and then covers the following topics: Markov chains, Markov decision processes, jump Markov processes, elements of queueing theory, basic renewal theory, elements of time series and simulation. Rigorous proofs are often replaced with sketches of arguments - with indications as to why a particular result holds, and also how it is connected with oth...

Most biologists use nonlinear regression more than any other statistical technique, but there are very few places to learn about curve-fitting. This book, by the author of the very successful Intuitive Biostatistics, addresses this relatively focused need of an extraordinarily broad range of scientists. The book will likely be purchased by a high proportion of biological laboratories, for frequent reference. The author gets about 3000 visits per month to his curvefit website, with the average v...

Statistical reasoning and modeling are of critical importance to modern biology. This textbook introduces fundamental concepts from probability and statistics which will pave the way for the student of biology to become a well-rounded scientist. No previous study of probability or statistics is assumed. Calculus topics are not used extensively in this book, though some integration and differentiation are expected. The calculus prerequisite is primarily intended to assure a certain level of mathe...

Berk has incisively identified the various strains of regression abuse and suggests practical steps for researchers who desire to do good social science while avoiding such errors." --Peter H. Rossi, University of Massachusetts, Amherst "I have been waiting for a book like this for some time. Practitioners, especially those doing applied work, will have much to gain from Berk′s volume, regardless of their level of statistical sophistication. Graduate students in sociology, education, public...

Statistical Concepts consists of the last 9 chapters of An Introduction to Statistical Concepts, 3rd ed. Designed for the second course in statistics, it is one of the few texts that focuses just on intermediate statistics. The book highlights how statistics work and what they mean to better prepare students to analyze their own data and interpret SPSS and research results. As such it offers more coverage of non-parametric procedures used when standard assumptions are violated since these method...

Concise, mathematically clear, and comprehensive treatment of the subject. * Expanded coverage of diagnostics and methods of model fitting. * Requires no specialized knowledge beyond a good grasp of matrix algebra and some acquaintance with straight-line regression and simple analysis of variance models. * More than 200 problems throughout the book plus outline solutions for the exercises. * This revision has been extensively class-tested.

This book presents a unified approach on nonparametric estimators for models of independent observations, jump processes and continuous processes. New estimators are defined and their limiting behavior is studied. From a practical point of view, the book expounds on the construction of estimators for functionals of processes and densities, and provides asymptotic expansions and optimality properties from smooth estimators.It also presents new regular estimators for functionals of processes, com...

The book starts with a range of examples and develops techniques progressively, starting with standard least squares prediction of a single variable from another and moving onto shrinkage techniques for multiple variables. Chapters 6 and 7 refer mostly to methods that have been specifically developed for spectroscopy. The other chapters are quite general in their applicability. Likelihood and Bayesian inference features strongly, the latter allowing flexible analysis of a wide range of multiva...