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

By: Spiegelhalter, D.J [author]Material type: Text

TextPublisher: 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	High School Learning Resource Center General Circulation	QA276.12 .S65 2019 (Browse shelf)	1	Available		HS14307

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Previous	No cover image available	No cover image available					No cover image available	Next
Previous	QA 251 S54 1980 Elementary linear algebra	QA 266 L35 1972 Binary	QA276.12 .B58 2025 Elementary statistics: a step by step approach: a brief version/	QA276.12 .S65 2019 The art of statistic : how to learn from data /	QA276.2 .C73 2023 AP statistics prep. /	QA276.2.S72 2022 Statistics: 1001 practice problems for dummies	QA 276.4 L48 1983 A STATLIB primer : the forecasting process through statistical computing	Next

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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