Statistics and Analysis of Scientific Data, 2nd Edition

Authors: Massimiliano Bonamente



Introduces the statistical techniques most commonly employed in physical sciences and engineering

Makes clear distinction between material that is strictly mathematical and theoretical, and practical applications of statistical methods

Expanded to cover selected core statistical methods used in business science

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.

Table of contents (16 chapters)

Front Matter
Pages i-xvii

Theory of Probability
Pages 1-15

Random Variables and Their Distributions
Pages 17-33

Three Fundamental Distributions: Binomial, Gaussian, and Poisson
Pages 35-54

Functions of Random Variables and Error Propagation
Pages 55-83

Maximum Likelihood and Other Methods to Estimate Variables
Pages 85-106

Mean, Median, and Average Values of Variables
Pages 107-115

Hypothesis Testing and Statistics
Pages 117-146

Maximum Likelihood Methods for Two-Variable Datasets
Pages 147-164

Multi-Variable Regression
Pages 165-175

Goodness of Fit and Parameter Uncertainty
Pages 177-193

Systematic Errors and Intrinsic Scatter
Pages 195-201

Fitting Two-Variable Datasets with Bivariate Errors
Pages 203-210

Model Comparison
Pages 211-223

Monte Carlo Methods
Pages 225-236

Introduction to Markov Chains
Pages 237-247

Monte Carlo Markov Chains
Pages 249-271

Back Matter
Pages 273-318