The OpenIntro project was founded in 2009 to improve the quality and availability of education by producing exceptional books and teaching tools that are free to use and easy to modify. We feature real data whenever possible, and files for the entire textbook are freely available at openintro.org. Visit our website, openintro.org. We provide free videos, statistical software labs, lecture slides, course management tools, and many other helpful resources.

Classic analysis of the subject and the development of personal probability; one of the greatest controversies in modern statistcal thought. New preface and new footnotes to 1954 edition, with a supplementary 180-item annotated bibliography by author. Calculus, probability, statistics, and Boolean algebra are recommended.

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.

Introductory Statistics follows scope and sequence requirements of a one-semester introduction to statistics course and is geared toward students majoring in fields other than math or engineering. The text assumes some knowledge of intermediate algebra and focuses on statistics application over theory. Introductory Statistics includes innovative practical applications that make the text relevant and accessible, as well as collaborative exercises, technology integration problems, and statistics labs. Senior Contributing Authors Barbara Illowsky, De Anza College Susan Dean, De Anza College Contributing Authors Daniel Birmajer, Nazareth College Bryan Blount, Kentucky Wesleyan College Sheri Boyd, Rollins College Matthew Einsohn, Prescott College James Helmreich, Marist College Lynette Kenyon, Collin County Community College Sheldon Lee, Viterbo University Jeff Taub, Maine Maritime Academy

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.

Statistics and the Media: Foundations in Statistical Thinking through Media Examples uses real-world examples from various media to give students an introduction to fundamentals of statistical thinking. Unlike many standard texts in the discipline, the book focuses on conceptual understanding - the meaning behind mathematical calculations rather than the calculations themselves. Written in accessible language, Statistics and the Media begins by discussing the importance of learning how research is conducted and the way research results, on any topic, are presented by the media. This understanding creates an essential context for subsequent chapters on surveys and polling, variation in measurement data, understanding probability, hypothesis testing, linear regression, and more. Students also learn how statistics can be manipulated by researchers to provide a desired result. An ideal supplement to any primary statistics text, Statistics and the Media helps readers view statistics as a common-sense, observational, fact-based way of thinking about the world. The book can be used in any course that deals with introductory statistics, particularly those in the social sciences, business, finance, and economics.

The ability to analyze and interpret enormous amounts of data has become a prerequisite for success in allied healthcare and the health sciences. Now in its 11th edition, Biostatistics: A Foundation for Analysis in the Health Sciences continues to offer in-depth guidance toward biostatistical concepts, techniques, and practical applications in the modern healthcare setting. Comprehensive in scope yet detailed in coverage, this text helps students understand—and appropriately use—probability distributions, sampling distributions, estimation, hypothesis testing, variance analysis, regression, correlation analysis, and other statistical tools fundamental to the science and practice of medicine. Clearly-defined pedagogical tools help students stay up-to-date on new material, and an emphasis on statistical software allows faster, more accurate calculation while putting the focus on the underlying concepts rather than the math. Students develop highly relevant skills in inferential and differential statistical techniques, equipping them with the ability to organize, summarize, and interpret large bodies of data. Suitable for both graduate and advanced undergraduate coursework, this text retains the rigor required for use as a professional reference.

OpenIntro is a new project. Statistics is our first book.David Diez and Christopher Barr founded the OpenIntro project in 2009 to improve the quality and availability of education by producing introductory books and teaching tools that are free to use and easy to modify. Since our first conversations, the project has grown to include a group of core Fellows - Filipp Brunshteyn, Mine Ãetinkaya, Rob Gould and Meenal Patel - and countless other collaborators. Together, our team hopes to lead a growing movement in open source education. Our inaugural effort is OpenIntro: Statistics.First and foremost, OpenIntro: Statistics (Preliminary Edition) strives to be a complete introductory textbook of the highest caliber. Its core derives from the classic notions of statistics education, and is extended by recent innovations. This, our first release, includes chapters in foundations of probability and statistical inference, and also reflects the increasing interest in simulations and applied data analysis. The approach is immersive and practical. We are proud to make the book available in paperback for less than 7 dollars from amazon.com and free in PDF from our homepage openintro.org. Visit us often to track our progress and get involved.David M Diez is a postdoctoral fellow and Christopher D Barr is an assistant professor - both in the Department of Biostatistics at the Harvard School of Public Health. Mine Ãetinkaya is a graduating doctoral student in the Department of Statistics at UCLA.

This book is a fresh approach to a calculus based, first course in probability and statistics, using R throughout to give a central role to data and simulation. The book introduces probability with Monte Carlo simulation as an essential tool. Simulation makes challenging probability questions quickly accessible and easily understandable. Mathematical approaches are included, using calculus when appropriate, but are always connected to experimental computations. Using R and simulation gives a nuanced understanding of statistical inference. The impact of departure from assumptions in statistical tests is emphasized, quantified using simulations, and demonstrated with real data. The book compares parametric and non-parametric methods through simulation, allowing for a thorough investigation of testing error and power. The text builds R skills from the outset, allowing modern methods of resampling and cross validation to be introduced along with traditional statistical techniques. Fifty-two data sets are included in the complementary R package fosdata. Most of these data sets are from recently published papers, so that you are working with current, real data, which is often large and messy. Two central chapters use powerful tidyverse tools (dplyr, ggplot2, tidyr, stringr) to wrangle data and produce meaningful visualizations. Preliminary versions of the book have been used for five semesters at Saint Louis University, and the majority of the more than 400 exercises have been classroom tested.