The art of statistics: learning from data
Spiegelhalter, David
The art of statistics: learning from data - UK Pelican 2019 - xvi, 426 p. Includes illustration, bibliographical references and index - A Pelican book .
Table of Content
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.
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.
https://www.penguin.co.uk/books/294/294857/learning-from-data/9780241258767.html
9780241258767
Descriptive statistics
Data mining
Algorithm - Analytics - Probability
519.5 / S7A7
The art of statistics: learning from data - UK Pelican 2019 - xvi, 426 p. Includes illustration, bibliographical references and index - A Pelican book .
Table of Content
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.
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.
https://www.penguin.co.uk/books/294/294857/learning-from-data/9780241258767.html
9780241258767
Descriptive statistics
Data mining
Algorithm - Analytics - Probability
519.5 / S7A7