Screen LibraryThe art of statistics : learning from data /
Statistics has played a leading role in our scientific understanding of the world for centuries, yet we are all familiar with the way statistical claims can be sensationalised, particularly in the media. In the age of big data, as data science becomes established as a discipline, a basic grasp of st...
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| Main Authors: | |
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| Published: |
Pelican, an imprint of Penguin Books,
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| Publisher Address: | [London] UK : |
| Publication Dates: | 2019. |
| Literature type: | Book |
| Language: | English |
| Series: |
Pelican books
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| Subjects: | |
| Summary: |
Statistics has played a leading role in our scientific understanding of the world for centuries, yet we are all familiar with the way statistical claims can be sensationalised, particularly in the media. In the age of big data, as data science becomes established as a discipline, a basic grasp of statistical literacy is more important than ever. In The Art of Statistics, David Spiegelhalter guides the reader through the essential principles we need in order to derive knowledge from data. Drawing on real world problems to introduce conceptual issues, he shows us how statistics can help us determine the luckiest passenger on the Titanic, whether serial killer Harold Shipman could have been caught earlier, and if screening for ovarian cancer is beneficial. How many trees are there on the planet? Do busier hospitals have higher survival rates? Why do old men have big ears? Spiegelhalter reveals the answers to these and many other questions - questions that can only be addressed using statistical science |
| Carrier Form: | xvi, 426 pages : illustrations ; 23 cm. |
| Bibliography: | Includes bibliographical references and index. |
| ISBN: |
9780241398630 0241398630 9780241258767 0241258766 |
| Index Number: | HA29 |
| CLC: | C8 |
| Call Number: | C8/S755 |
| Contents: | List of figures -- List of tables -- Acknowledgements -- 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 measurements -- What causes what? -- Modelling relationships using regression -- Algorithims, 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 -- Glossary -- Notes -- Index. |