A 75th anniversary e-book version of the most important and practical self-help book ever written, Alcoholics Anonymous. Here is a special deluxe edition of a book that has changed millions of lives and launched the modern recovery movement: Alcoholics Anonymous. This edition not only reproduces the original 1939 text of Alcoholics Anonymous, but as a special bonus features the complete 1941 Saturday Evening Post article “Alcoholics Anonymous” by journalist Jack Alexander, which, at the time, did as much as the book itself to introduce millions of seekers to AA’s program. Alcoholics Anonymous has touched and transformed myriad lives, and finally appears in a volume that honors its posterity and impact.

Make workplace conflict resolution a game that EVERYBODY wins! Recent studies show that typical managers devote more than a quarter of their time to resolving coworker disputes. The Big Book of Conflict-Resolution Games offers a wealth of activities and exercises for groups of any size that let you manage your business (instead of managing personalities). Part of the acclaimed, bestselling Big Books series, this guide offers step-by-step directions and customizable tools that empower you to heal rifts arising from ineffective communication, cultural/personality clashes, and other specific problem areas—before they affect your organization's bottom line. Let The Big Book of Conflict-Resolution Games help you to: Build trust Foster morale Improve processes Overcome diversity issues And more Dozens of physical and verbal activities help create a safe environment for teams to explore several common forms of conflict—and their resolution. Inexpensive, easy-to-implement, and proved effective at Fortune 500 corporations and mom-and-pop businesses alike, the exercises in The Big Book of Conflict-Resolution Games delivers everything you need to make your workplace more efficient, effective, and engaged.

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.

Beast Academy Guide 2A and its companion Practice 2A (sold separately) are the first part in the planned four-part series for 2nd grade mathematics. Book 2A includes chapters on place value, comparing, and addition.

This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data.

This book is designed to introduce doctoral and graduate students to the process of conducting scientific research in the social sciences, business, education, public health, and related disciplines. It is a one-stop, comprehensive, and compact source for foundational concepts in behavioral research, and can serve as a stand-alone text or as a supplement to research readings in any doctoral seminar or research methods class. This book is currently used as a research text at universities on six continents and will shortly be available in nine different languages.

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.