Loose Leaf Version For The Basic Practice Of Statistics

This is a clear and innovative overview of statistics which emphasises major ideas, essential skills and real-life data. The organisation and design has been improved for the fifth edition, coverage of engaging, real-world topics has been increased and content has been updated to appeal to today's trends and research.

This remarkably engaging textbook is the perfect learning resource for undergraduate and postgraduate biology students studying statistics and data analysis. Part of the best-selling Moore family of statistics books, it covers essential statistical topics with examples and exercises drawn from across the field of life sciences, including disciplines such as nursing, public health, and allied health. Based on David Moore’s classic The Basic Practice of Statistics, this textbook applies the bestseller’s signature emphasis on statistical thinking, real data and what statisticians actually do, to the world of life sciences, helping engage students and underlining how statistics can directly apply to the projects they’re working on. The new edition includes new and updated exercises, examples, and samples of real data, as well as an expanded range of media tools for students and instructors. This textbook is also available on LaunchPad.

"Learning Statistics with R" covers the contents of an introductory statistics class, as typically taught to undergraduate psychology students, focusing on the use of the R statistical software and adopting a light, conversational style throughout. The book discusses how to get started in R, and gives an introduction to data manipulation and writing scripts. From a statistical perspective, the book discusses descriptive statistics and graphing first, followed by chapters on probability theory, sampling and estimation, and null hypothesis testing. After introducing the theory, the book covers the analysis of contingency tables, t-tests, ANOVAs and regression. Bayesian statistics are covered at the end of the book. For more information (and the opportunity to check the book out before you buy!) visit http://ua.edu.au/ccs/teaching/lsr or http://learningstatisticswithr.com

CD-ROM includes: Electronic Encyclopedia of Statistical Examples and Exercises, an interactive quiz for each chapter, video clips and some special electronic statistical tools.

David Moore’s data analysis (conceptual) approach, which revolutionized the introductory statistics textbook, moves students away from formulas and number-crunching, focusing instead on how working statisticians in a variety of fields collect and analyze data, and use the results to tackle real-world problems. The clear, direct way of emphasizing the course’s relevance and confronting students’ math anxieties is at the heart of the bestselling The Basic Practice of Statistics (BPS). It is also the ideal approach for taking full advantage of the powerful statistical tools and interactive learning features in this new edition’s text/media package. Now more than ever, BPS is ready to help students move from reading about statistical practice to practicing statistics themselves.

No textbook communicates the basics of statistical analysis to liberal arts students as effectively as the bestselling Statistics: Concepts and Controversies (SCC). And no text makes it easier for these students to understand and talk about statistical claims they encounter in commercials, campaigns, the media, sports, and elsewhere in their lives. The new edition offers SCC’s signature combination of engaging cases, real-life examples and exercises, helpful pedagogy, rich full-color design, and innovative media learning tools, all significantly updated.

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.