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[null,"1.","Record Nr.",null,"UNINA9910462958503321"]
[null,null,"Autore",null,"Bevan Adrian"]
[null,null,"Titolo",null,"Statistical Data Analysis for the Physical Sciences / / Adrian Bevan, Queen Mary, University of London [[electronic resource]]"]
[null,null,"Pubbl/distr/stampa",null,"Cambridge : , : Cambridge University Press, , 2013"]
[null,null,"ISBN",null,"1-316-09069-8"]
[{"$nulls":4},"1-107-05690-X"]
[{"$nulls":4},"1-107-25564-3"]
[{"$nulls":4},"1-139-34281-9"]
[{"$nulls":4},"1-107-05803-1"]
[{"$nulls":4},"1-107-05930-5"]
[{"$nulls":4},"1-107-05580-6"]
[null,null,"Descrizione fisica",null,"1 online resource (xi, 220 pages) : digital, PDF file(s)"]
[null,null,"Disciplina",null,"530/.078"]
[null,null,"Soggetti",null,"Physical sciences - Statistical methods"]
[{"$nulls":4},"Physical sciences - Experiments - Data processing"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Title from publisher's bibliographic system (viewed on 05 Oct 2015)."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Introduction -- Sets -- Probability -- Visualising and quantifying the properties of data -- Useful distributions -- Uncertainty and errors -- Confidence intervals -- Hypothesis testing -- Fitting -- Multivariate analysis -- Appendix A. Glossary -- Appendix B. Probability density functions -- Appendix C. Numerical integration methods -- Appendix D. Solutions -- Appendix E. Reference tables -- References -- Index."]
[null,null,null,"Sommario/riassunto","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."]