9781107024151-1107024153-Measuring and Reasoning: Numerical Inference in the Sciences

Measuring and Reasoning: Numerical Inference in the Sciences

ISBN-13: 9781107024151
ISBN-10: 1107024153
Edition: 1
Author: Fred L. Bookstein
Publication date: 2014
Publisher: Cambridge University Press
Format: Hardcover 559 pages
FREE US shipping
Buy

From $64.99

Book details

ISBN-13: 9781107024151
ISBN-10: 1107024153
Edition: 1
Author: Fred L. Bookstein
Publication date: 2014
Publisher: Cambridge University Press
Format: Hardcover 559 pages

Summary

Measuring and Reasoning: Numerical Inference in the Sciences (ISBN-13: 9781107024151 and ISBN-10: 1107024153), written by authors Fred L. Bookstein, was published by Cambridge University Press in 2014. With an overall rating of 3.8 stars, it's a notable title among other Applied (Mathematics) books. You can easily purchase or rent Measuring and Reasoning: Numerical Inference in the Sciences (Hardcover) from BooksRun, along with many other new and used Applied books and textbooks. And, if you're looking to sell your copy, our current buyback offer is $0.62.

Description

In Measuring and Reasoning, Fred L. Bookstein examines the way ordinary arithmetic and numerical patterns are translated into scientific understanding, showing how the process relies on two carefully managed forms of argument: * Abduction: the generation of new hypotheses to accord with findings that were surprising on previous hypotheses, and * Consilience: the confirmation of numerical pattern claims by analogous findings at other levels of measurement. These profound principles include an understanding of the role of arithmetic and, more importantly, of how numerical patterns found in one study can relate to numbers found in others. They are illustrated through numerous classic and contemporary examples arising in disciplines ranging from atomic physics through geosciences to social psychology. The author goes on to teach core techniques of pattern analysis, including regression and correlation, normal distributions, and inference, and shows how these accord with abduction and consilience, first in the simple setting of one dependent variable and then in studies of image data for complex or interdependent systems. More than 200 figures and diagrams illuminate the text. The book can be read with profit by any student of the empirical natural or social sciences and by anyone concerned with how scientists persuade those of us who are not scientists why we should credit the most important claims about scientific facts or theories.

Rate this book Rate this book

We would LOVE it if you could help us and other readers by reviewing the book