Statistics and analysis of scientific data / Massimiliano Bonamente.

Format:

Book

Author/Creator:

Bonamente, Massimiliano, author.

Series:

Graduate texts in physics

Language:

English

Subjects (All):

Mathematical statistics.

Research--Statistical methods.

Research.

Genre:

Electronic books.

Physical Description:

1 online resource (491 pages)

Edition:

3rd ed.

Place of Publication:

Singapore : Springer, 2022.

Summary:

This book is the third edition of a successful textbook for upper-undergraduate and early graduate students, which offers a solid foundation in probability theory and statistics and their application to physical sciences, engineering, biomedical sciences and related disciplines. It provides broad coverage ranging from conventional textbook content of probability theory, random variables, and their statistics, regression, and parameter estimation, to modern methods including Monte-Carlo Markov chains, resampling methods and low-count statistics. In addition to minor corrections and adjusting structure of the content, particular features in this new edition include: Python codes and machine-readable data for all examples, classic experiments, and exercises, which are now more accessible to students and instructors New chapters on low-count statistics including the Poisson-based Cash statistic for regression in the low-count regime, and on contingency tables and diagnostic testing. An additional example of classic experiments based on testing data for SARS-COV-2 to demonstrate practical applications of the described statistical methods. This edition inherits the main pedagogical method of earlier versionsa theory-then-application approachwhere 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 materials. 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.

Contents:

Theory of Probability

Random Variables and Their Distributions

Three Fundamental Distributions: Binomial, Gaussian and Poisson

The Distribution of Functions of Random Variables

Error Propagation and Simulation of Random Variables

Maximum Likelihood and Other Methods to Estimate Variables

Mean, Median and Average Values of Variables

Hypothesis Testing and Statistics

Maximumlikelihood Methods for Gaussian Data

Multivariable Regression and Generalized Linear Models

Goodness of Fit and Parameter Uncertainty for Gaussian Data

LowCount Statistics

Maximumlikelihood Methods for lowcount Statistics

The linear Correlation Coefficient

Systematic Errors and Intrinsic Scatter.-Regression with Bivariate Errors

Model Comparison

Monte Carlo Methods

Introduction to Markov Chains

Monte Carlo Markov Chains.

Notes:

Includes bibliographical references and index.

Online resource; title from PDF title page (SpringerLink, viewed July 22, 2022).

Other Format:

Print version: Bonamente, Massimiliano. Statistics and Analysis of Scientific Data.

ISBN:

9789811903656

9811903654

OCLC:

1336403383

Access Restriction:

Restricted for use by site license.