The Making Of Statisticians
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The Making of Statisticians
Author: J. Gani
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-12-06
Like many other scientists, I have long been interested in history. I enjoy reading about the minutiae of its daily unfolding: the coinage, food, clothes, games, literature and habits which characterize a people. I am carried away by the broad sweep of its major events: the wars, famines, migrations, reforms, political swings and scientific advances which shape a society. I know that historians value autobiographical accounts as part of the basic material from which the stuff of history is distilled; this should apply no less to statistical than to political or social history. Modem statistics is a relatively young science; it was while pondering this fact sometime in 1980 that I realized that many of the pioneers of our field could still be called upon to tell their stories. If, however, biographical material about these eminent statisticians was not gathered, then one might lose the chance to gain insight into the origins of many an important statistical development. The remarkable experience of these colleagues could not be readily duplicated. Fired by these thoughts, I took it upon myself to plan the framework of this book. In it, eminent statisticians (probabilists are included under this title) would be invited to sketch their lives, explain how they had become interested in probability and· statistics, give an account of their major contributions, and possibly hazard some predictions about the future of the subject.
Translating Statistics to Make Decisions
Examine and solve the common misconceptions and fallacies that non-statisticians bring to their interpretation of statistical results. Explore the many pitfalls that non-statisticians—and also statisticians who present statistical reports to non-statisticians—must avoid if statistical results are to be correctly used for evidence-based business decision making. Victoria Cox, senior statistician at the United Kingdom’s Defence Science and Technology Laboratory (Dstl), distills the lessons of her long experience presenting the actionable results of complex statistical studies to users of widely varying statistical sophistication across many disciplines: from scientists, engineers, analysts, and information technologists to executives, military personnel, project managers, and officials across UK government departments, industry, academia, and international partners. The author shows how faulty statistical reasoning often undermines the utility of statistical results even among those with advanced technical training. Translating Statistics teaches statistically naive readers enough about statistical questions, methods, models, assumptions, and statements that they will be able to extract the practical message from statistical reports and better constrain what conclusions cannot be made from the results. To non-statisticians with some statistical training, this book offers brush-ups, reminders, and tips for the proper use of statistics and solutions to common errors. To fellow statisticians, the author demonstrates how to present statistical output to non-statisticians to ensure that the statistical results are correctly understood and properly applied to real-world tasks and decisions. The book avoids algebra and proofs, but it does supply code written in R for those readers who are motivated to work out examples. Pointing along the way to instructive examples of statistics gone awry, Translating Statistics walksreaders through the typical course of a statistical study, progressing from the experimental design stage through the data collection process, exploratory data analysis, descriptive statistics, uncertainty, hypothesis testing, statistical modelling and multivariate methods, to graphs suitable for final presentation. The steady focus throughout the book is on how to turn the mathematical artefacts and specialist jargon that are second nature to statisticians into plain English for corporate customers and stakeholders. The final chapter neatly summarizes the book’s lessons and insights for accurately communicating statistical reports to the non-statisticians who commission and act on them. What You'll Learn Recognize and avoid common errors and misconceptions that cause statistical studies to be misinterpreted and misused by non-statisticians in organizational settings Gain a practical understanding of the methods, processes, capabilities,and caveats of statistical studies to improve the application of statistical data to business decisions See how to code statistical solutions in R Who This Book Is For Non-statisticians—including both those with and without an introductory statistics course under their belts—who consume statistical reports in organizational settings, and statisticians who seek guidance for reporting statistical studies to non-statisticians in ways that will be accurately understood and will inform sound business and technical decisions
All of Statistics
Author: Larry Wasserman
language: en
Publisher: Springer Science & Business Media
Release Date: 2013-12-11
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.