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The art of statistic : how to learn from data / David J. Spiegelhalter

By: Spiegelhalter, D.J [author]Material type: TextTextPublisher: New York : Basic Books, c2019Edition: First editionDescription: xvi, 426 pages : illustrations ; 25 cmContent type: text Media type: unmediated Carrier type: volumeISBN: 9781541675704Subject(s): Statistics -- Popular worksLOC classification: QA276.12 | .S65 2019
Contents:
Getting things done in proportion : categorical data and percentages -- 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 --
Summary: Shows how to apply statistical reasoning to real-world problems. This isn't simply memorizing formulas or using the tools in a spreadsheet: he emphasizes the importance of clarifying questions, assumptions, and expectations, and--more importantly--knowing how to responsibly interpret the results the software generates.--
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Item type Current location Call number Copy number Status Date due Barcode
Books Books High School Learning Resource Center
General Circulation 		QA276.12 .S65 2019 (Browse shelf) 1 Available HS14307

Includes bibliographical references (pages 407-418) and index.

Introduction Getting things done in proportion : categorical data and percentages --
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 --

Shows how to apply statistical reasoning to real-world problems. This isn't simply memorizing formulas or using the tools in a spreadsheet: he emphasizes the importance of clarifying questions, assumptions, and expectations, and--more importantly--knowing how to responsibly interpret the results the software generates.--

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