Statistical data analysis for the physical sciences
Bevan, Adrian
Statistical data analysis for the physical sciences - United Kingdom: Cambridge University Press, 2013. - xi, 220p.; 23cms.
Data analysis lies at the heart of every experimental science. Providing a modern introduction to statistics, this book is ideal for undergraduates in physics. It introduces the necessary tools required to analyse data from experiments across a range of areas, making it a valuable resource for students. In addition to covering the basic topics, the book also takes in advanced and modern subjects, such as neural networks, decision trees, fitting techniques and issues concerning limit or interval setting. Worked examples and case studies illustrate the techniques presented, and end-of-chapter exercises help test the reader's understanding of the material.
Combines formal descriptions of methods with examples and case studies to illustrate how those methods may be used
Includes a chapter on multivariate analysis to introduce the concept of data mining in terms of cut-based analysis, Bayesian classifiers, the Fisher discriminant, neural networks and decision trees
Over 130 exercises, with solutions provided, help readers test their understanding of the subject and understand the material covered
Table of contents:
Preface
1. Introduction
2. Sets
3. Probability
4. Visualising and quantifying the properties of data
5. Useful distributions
6. Uncertainty and errors
7. Confidence intervals
8. Hypothesis testing
9. Fitting
10. Multivariate analysis
Appendixes
References
Index
9781107670341
Statistical data analysis
Physical sciences--Multivariate analysis
Visualising and quantifying the properties of data
519.50245 B48
Statistical data analysis for the physical sciences - United Kingdom: Cambridge University Press, 2013. - xi, 220p.; 23cms.
Data analysis lies at the heart of every experimental science. Providing a modern introduction to statistics, this book is ideal for undergraduates in physics. It introduces the necessary tools required to analyse data from experiments across a range of areas, making it a valuable resource for students. In addition to covering the basic topics, the book also takes in advanced and modern subjects, such as neural networks, decision trees, fitting techniques and issues concerning limit or interval setting. Worked examples and case studies illustrate the techniques presented, and end-of-chapter exercises help test the reader's understanding of the material.
Combines formal descriptions of methods with examples and case studies to illustrate how those methods may be used
Includes a chapter on multivariate analysis to introduce the concept of data mining in terms of cut-based analysis, Bayesian classifiers, the Fisher discriminant, neural networks and decision trees
Over 130 exercises, with solutions provided, help readers test their understanding of the subject and understand the material covered
Table of contents:
Preface
1. Introduction
2. Sets
3. Probability
4. Visualising and quantifying the properties of data
5. Useful distributions
6. Uncertainty and errors
7. Confidence intervals
8. Hypothesis testing
9. Fitting
10. Multivariate analysis
Appendixes
References
Index
9781107670341
Statistical data analysis
Physical sciences--Multivariate analysis
Visualising and quantifying the properties of data
519.50245 B48