Logo

Statistical Treatment of Experimental Data

Large book cover: Statistical Treatment of Experimental Data

Statistical Treatment of Experimental Data
by

Publisher: McGraw Hill
ISBN/ASIN: 088133913X

Description:
Even with a limited mathematics background, readers can understand what statistical methods are and how they may be used to obtain the best possible results from experimental measurements and data. The author describes the physical bases on which statistical theories are developed, and derives from them useful mathematical results and formulas for the evaluation and analysis of experimental data. Special mathematical techniques are explained as they are needed.

Home page url

Download or read it online for free here:
Download link
(multiple PDF files)

Similar books

Book cover: Dynamic Programming and Bayesian Inference: Concepts and ApplicationsDynamic Programming and Bayesian Inference: Concepts and Applications
by - InTech
Dynamic programming and Bayesian inference have been both intensively and extensively developed during recent years. The purpose of this volume is to provide some applications of Bayesian optimization and dynamic programming.
(8213 views)
Book cover: Multivariate Statistics: Concepts, Models, and ApplicationsMultivariate Statistics: Concepts, Models, and Applications
by - Missouri State University
The book for a course in multivariate statistics for first year graduate or advanced undergraduates. It is neither a mathematical treatise nor a cookbook. Instead of complicated mathematical proofs the author wrote about mathematical ideas.
(14711 views)
Book cover: Statistics Beyond the Absolute BasicsStatistics Beyond the Absolute Basics
by - scientificlanguage.com
The book begins by expanding on some of the basic concepts such data types and variables. The basic choice then is between the family of statistics which compares groups, and the family which studies associations or correlations.
(6156 views)
Book cover: Causal InferenceCausal Inference
by - Chapman & Hall/CRC
The book provides a cohesive presentation of concepts of, and methods for, causal inference. It will be of interest to anyone interested in causal inference, e.g., epidemiologists, statisticians, psychologists, economists, sociologists, and others.
(10308 views)