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

By: Material type: TextNew York : Basic Books, 2021Edition: First US Trade Paperback editionDescription: xvi, 426 pages : illustrations ; 21 cmContent type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
ISBN:
  • 9781541675704
Subject(s): DDC classification:
  • 23 519.5
LOC classification:
  • QA276.12 .S654 2021
Contents:
Introduction -- Getting things done 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.
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.-- Source other than the Library of Congress.
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Holdings
Cover image Item type Current library Home library Collection Shelving location Call number Materials specified Vol info URL Copy number Status Notes Date due Barcode Item holds Item hold queue priority Course reserves
Books High School Learning Resource Center General Circulation Non-fiction QA 276.12 .S654 2021 (Browse shelf(Opens below)) 1 Available HS14799

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

Introduction -- Getting things done 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.

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.-- Source other than the Library of Congress.

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