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Macmillan Higher Education Palgrave Higher Education

Statistics and Analysis of Scientific Data

Edition 2nd Edition
ISBN 9781493965700
Publication Date December 2016
Formats Hardcover Ebook 
Publisher Springer

The revised second edition of this textbook provides the reader with a solid foundation in probability theory and statistics as applied to the physical sciences, engineering and related fields. It covers a broad range of numerical and analytical methods that are essential for the correct analysis of scientific data, including probability theory, distribution functions of statistics, fits to two-dimensional data and parameter estimation, Monte Carlo methods and Markov chains. 

Features new to this edition include: 

• a discussion of statistical techniques employed in business science, such as multiple regression analysis of multivariate datasets.
• a new chapter on the various measures of the mean including logarithmic averages.
• new chapters on systematic errors and intrinsic scatter, and on the fitting of data with bivariate errors.
• a new case study and additional worked examples.
• mathematical derivations and theoretical background material have been appropriately marked, to improve the readability of the text.
• end-of-chapter summary boxes, for easy reference.

As in the first edition, the main pedagogical method is a theory-then-application approach, where emphasis is placed first on a sound understanding of the underlying theory of a topic, which becomes the basis for an efficient and practical application of the material. The level is appropriate for undergraduates and beginning graduate students, and as a reference for the experienced researcher. Basic calculus is used in some of the derivations, and no previous background in probability and statistics is required. The book includes many numerical tables of data, as well as exercises and examples to aid the readers' understanding of the topic.

Max Bonamente is Professor of Astrophysics at the University of Alabama, Huntsville.

Theory of Probability
Random Variables and Their Distribution
Sum and Functions of Random Variables
Estimate of Mean and Variance and Confidence Intervals
Median, Weighted Mean and Linear Average (NEW)
Distribution Function of Statistics and Hypothesis Testing
Maximum Likelihood Fit to a Two-Variable Dataset
Goodness of Fit and Parameter Uncertainty
Systematic Errors and Intrinsic Scatter (NEW)
Fitting Data with Bivariate Errors (NEW)
Comparison Between Models
Monte Carlo Methods
Markov Chains and Monte Carlo Markov Chains
Statistics for Business Sciences and Addition of Multi–Variate Analysis (NEW).

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