Portland Public Library

The art of statistics, how to learn from data, David Spiegelhalter

eng

Includes bibliographical references (pages 407-418) and index

illustrations

index present

non fiction

The art of statistics

bibliography

1112668483

David Spiegelhalter

how to learn from data

This book shows how to apply statistical reasoning to real-world problems. This isn't simply memorizing formulas or using the tools in a spreadsheet. The author emphasizes the importance of clarifying questions, assumptions, and expectations, and - more importantly - knowing how to responsibly interpret the results the software generatesStatistics are everywhere, as integral to science as they are to business, and in the popular media hundreds of times a day. In this age of big data, a basic grasp of statistical literacy is more important than ever if we want to separate the fact from the fiction, the ostentatious embellishments from the raw evidence - and even more so if we hope to participate in the future, rather than being simple bystanders. In this book, a renowned statistician shows readers how to derive knowledge from raw data by focusing on the concepts and connections behind the math. Drawing on real world examples to introduce complex issues, he shows us how statistics can help us determine the luckiest passenger on the Titanic, whether a notorious serial killer could have been caught earlier, and if screening for ovarian cancer is beneficial. This book not only shows how mathematicians have used statistical science to solve these problems - it teaches how we too can think like statisticians. Readers will learn how to clarify their questions, assumptions, and expectations when approaching a problem, and - perhaps even more importantly - will learn how to responsibly interpret answers. Combining the incomparable insight of an expert with the playful enthusiasm of an aficionado, this book is a guide to properly applying statistics to address real-world problems. -- Adapted from publisher's description

Introduction -- Getting things in proportion : categorical data and percentages -- Summarizing and communicating numbers : Lots of numbers -- Why are we looking at data anyway? : Populations and measurement -- What causes what? -- Modelling relationships using regression -- Algorithms, analytics and prediction -- How sure can we be about what is going on? : Estimates and intervals -- Probability : the language of uncertainty and variability -- Putting probability and statistics together -- Answering questions and claiming discoveries -- Learning from experience the Bayesian way -- How things go wrong -- How we can do statistics better -- In conclusion

How to learn from data