Data Analysis: Statistical and Computational Methods for Scientists and Engineers

by Siegmund Brandt, Glen Gowan (Translator)

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.

686 pages, Hardcover

First published November 25, 1998

Reviews

[Amr Khaled · Rating 1 out of 5]

Right off the bat, it was very obvious that this book required a heavy backbone of statistics, one that I unfortunately do not have.
I stuck with it in the hopes that it might enlighten me about those weaknesses of knowledge that I have, alas that did not happen.
By the end, the book kept rambling about equations which I only saw in them the demise of my dreams to understand them.
My hope is for another book to salvage me.