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Written in a simple style that requires no advanced mathematical or statistical background, A First Course in Order Statistics introduces the general theory of order statistics and their applications. The book covers topics such as distribution theory for order statistics from continuous and discrete populations, moment relations, bounds and approximations, order statistics in statistical inference and characterization results, and basic asymptotic theory. There is also a short introduction to record values and related statistics. The authors have updated the text with suggestions for further reading that readers may use for self-study. Audience: advanced undergraduate and graduate students in statistics and mathematics, practicing statisticians, engineers, climatologists, economists, and biologists.
This updated classic text will aid readers in understanding much of the current literature on order statistics: a flourishing field of study that is essential for any practising statistician and a vital part of the training for students in statistics. Written in a simple style that requires no advanced mathematical or statistical background, the book introduces the general theory of order statistics and their applications. The book covers topics such as distribution theory for order statistics from continuous and discrete populations, moment relations, bounds and approximations, order statistics in statistical inference and characterisation results, and basic asymptotic theory. There is also a short introduction to record values and related statistics. The authors have updated the text with suggestions for further reading that may be used for self-study. Written for advanced undergraduate and graduate students in statistics and mathematics, practising statisticians, engineers, climatologists, economists, and biologists.
This volume provides an up-to-date coverage of the theory and applications of ordered random variables and their functions. Furthermore, it develops the distribution theory of OS systematically. Applications include procedures for the treatment of outliers and other data analysis techniques. Even when chapter and section headings are the same as in OSII, there are appreciable changes, mostly additions, with some obvious deletions. Parts of old Ch. 7, for example, are prime candidates for omission. Appendices are designed to help collate tables, computer algorithms, and software, as well as to compile related monographs on the subject matter. Extensive exercise sets will continue, many of them replaced by newer ones.
A self-contained introduction to probability, exchangeability and Bayes’ rule provides a theoretical understanding of the applied material. Numerous examples with R-code that can be run "as-is" allow the reader to perform the data analyses themselves. The development of Monte Carlo and Markov chain Monte Carlo methods in the context of data analysis examples provides motivation for these computational methods.
This introduction to statistics helps readers develop and enhance their critical thinking skills. It shows readers how to analyze data that appear in situations in the world around them and features an abundance of examples and exercisesnearly all based on current, real-world applications pulled from journals, magazines, news articles, and commerce. In addition, this book exposes readers to the most recent statistical software packages that will prove helpful on the job. Presenting balanced coverage of both the theory and application of statistics, the book discusses methods for describing data sets; probability; random variables and probability distributions; inferences based on a single sample utilizing tests of hypothesis and confidence intervals; comparing population proportions and means; simple linear regression, and much more. For business, engineering, and science professionals.
The Book of R is a comprehensive, beginner-friendly guide to R, the world’s most popular programming language for statistical analysis. Even if you have no programming experience and little more than a grounding in the basics of mathematics, you’ll find everything you need to begin using R effectively for statistical analysis. You’ll start with the basics, like how to handle data and write simple programs, before moving on to more advanced topics, like producing statistical summaries of your data and performing statistical tests and modeling. You’ll even learn how to create impressive data visualizations with R’s basic graphics tools and contributed packages, like ggplot2 and ggvis, as well as interactive 3D visualizations using the rgl package. Dozens of hands-on exercises (with downloadable solutions) take you from theory to practice, as you learn: –The fundamentals of programming in R, including how to write data frames, create functions, and use variables, statements, and loops –Statistical concepts like exploratory data analysis, probabilities, hypothesis tests, and regression modeling, and how to execute them in R –How to access R’s thousands of functions, libraries, and data sets –How to draw valid and useful conclusions from your data –How to create publication-quality graphics of your results Combining detailed explanations with real-world examples and exercises, this book will provide you with a solid understanding of both statistics and the depth of R’s functionality. Make The Book of R your doorway into the growing world of data analysis.
A lot of probabilists and statisticians, as well as actuarial mathematicians, reliability engineers, meteorologists, hydrologists, economists, business and sport analysts, very often deal with order statistics, which plays an important role in various fields of statistics and its applications. The authors suggest an approach to studying order statistics which proposes an active participation of our reader in the studying process. This book enables a reader to check his/her level of understanding of the theory of order statistics. It gives basic formulae (without proof), which are most important in the theory, and presents a lot of examples, which illustrate the theoretical statements.
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. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like non-parametric curve estimation, bootstrapping, and classification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analysing data.
The main difference between this text and many others is that an attempt is made here to present material in a rather relaxed and informal way without omitting important concepts. The text demonstrates the wide range of relevant issues and questions that can be addressed with the help of statistical analysis techniques by presenting over 1,750 realistic problems that arise often in health care, the social and physical sciences, education, business and economics, engineering, and leisure activities. It also convinces your students that statistics is "do-able" by including real data that students have collected and analyzed for class assignments and projects. Additionally, the text utilizes an intuitive, common sense approach (including occasional humorous situation or ridiculous name) to develop concepts whenever possible. "Statistics: A First Course" employs widely available, inexpensive technologies--particularly Minitab and the TI-83 graphing calculator. We also explore the use of the World Wide Web to collect data, providing students with the means to obtain up-to-date information without leaving their desks. In short this book is written to communicate with students rather than to lecture to them, and its intent is to convince readers that the study of statistics can be a lively, interesting, and rewarding experience!