Providing the knowledge and practical experience to begin analysing scientific data, this book is ideal for physical sciences students wishing to improve their data handling skills. The book focuses on explaining and developing the practice and understanding of basic statistical analysis, concentrating on a few core ideas, such as the visual display of information, modelling using the likelihood function, and simulating random data. Key concepts are developed through a combination of graphical explanations, worked examples, example computer code and case studies using real data. Students will develop an understanding of the ideas behind statistical methods and gain experience in applying them in practice. Further resources are available at www.cambridge.org/9781107607590, including data files for the case studies so students can practise analysing data, and exercises to test students' understanding.Read more
- Features several complete data analysis case studies using real data from great scientific experiments
- Boxed text contains worked examples and computer codes to help further understanding of the concepts covered in the main text
- Presents a self-contained introduction to statistical computing using the freely available R language
Reviews & endorsements
'… succinct and fast-paced …' Bogdan Hoanca, Optics and Photonics NewsSee more reviews
'… provides innovative and intelligent comments and connecting elements, as well as data analysis and interpretation … [it] extends to fundamental and known issues, which are offered from an understandable point of view.' Nikolaos E. Myridis, Contemporary Physics
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- Date Published: September 2013
- format: Hardback
- isbn: 9781107024823
- length: 236 pages
- dimensions: 252 x 178 x 15 mm
- weight: 0.61kg
- contains: 63 b/w illus.
- availability: In stock
Table of Contents
1. Science and statistical data analysis
2. Statistical summaries of data
3. Simple statistical inferences
4. Probability theory
5. Random variables
6. Estimation and maximum likelihood
7. Significance tests and confidence intervals
8. Monte Carlo methods
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