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Data Analysis - Statistical and Computational Methods for Scientists and Engineers (Paperback, Softcover reprint of the... Data Analysis - Statistical and Computational Methods for Scientists and Engineers (Paperback, Softcover reprint of the original 3rd ed. 1999)
Glen Gowan; Siegmund Brandt
R1,682 Discovery Miles 16 820 Ships in 10 - 15 working days

Bridging the gap between statistical theory and physical experiment, this is a thorough introduction to the statistical methods used in the experimental physical sciences and to the numerical methods used to implement them. The treatment emphasises concise but rigorous mathematics but always retains its focus on applications. Readers are assumed to have a sound basic knowledge of differential and integral calculus and some knowledge of vectors and matrices. After an introduction to probability, random variables, computer generation of random numbers and important distributions, the book turns to statistical samples, the maximum likelihood method, and the testing of statistical hypotheses. The discussion concludes with several important statistical methods: least squares, analysis of variance, polynomial regression, and analysis of time series. Appendices provide the necessary methods of matrix algebra, combinatorics, and many sets of useful algorithms and formulae.

Data Analysis - Statistical and Computational Methods for Scientists and Engineers (Hardcover, 3rd ed. 1999): Glen Gowan Data Analysis - Statistical and Computational Methods for Scientists and Engineers (Hardcover, 3rd ed. 1999)
Glen Gowan; Siegmund Brandt
R1,993 Discovery Miles 19 930 Ships in 10 - 15 working days

Bridging the gap between statistical theory and physical experiment, this is a thorough introduction to the statistical methods used in the experimental physical sciences and to the numerical methods used to implement them. An accompanying CD-ROM provides detailed code for implementing many of these algorithms. The treatment emphasises concise but rigorous mathematics but always retains its focus on applications. Readers are assumed to have a sound basic knowledge of differential and integral calculus and some knowledge of vectors and matrices. After an introduction to probability, random variables, computer generation of random numbers and important distributions, the book turns to statistical samples, the maximum likelihood method, and the testing of statistical hypotheses. The discussion concludes with several important statistical methods: least squares, analysis of variance, polynomial regression, and analysis of time series. Appendices provide the necessary methods of matrix algebra, combinatorics, and many sets of useful algorithms and formulae.

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