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Measuring and reasoning : = numerica...

Bookstein, Fred L., (1947-)

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Measuring and reasoning : = numerical inference in the sciences /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Measuring and reasoning :/ Fred L. Bookstein.
Reminder of title:	numerical inference in the sciences /
Author:	Bookstein, Fred L.,
Published:	New York :Cambridge University Press, : 2014.,
Description:	xxviii, 535 p., [4] p. of plates :ill. (some col.), maps (some col.) ;24 cm.
Subject:	Statistical hypothesis testing. -
ISBN:	9781107024151

Measuring and reasoning : = numerical inference in the sciences /
Bookstein, Fred L.,1947-

Measuring and reasoning :numerical inference in the sciences /Fred L. Bookstein. - New York :Cambridge University Press,2014. - xxviii, 535 p., [4] p. of plates :ill. (some col.), maps (some col.) ;24 cm.

Includes bibliographical references (p. 501-518) and index.

Getting started -- The basic structure of a numerical inference --

This exploration of empirical inference in science presents a formal description of the process by which scientific measurements support convincing explanations of the world around us"--

ISBN: 9781107024151US75.00

LCCN: 2013030370Subjects--Topical Terms:

560235
Statistical hypothesis testing.

LC Class. No.: QA277 / .B66 2014

Dewey Class. No.: 519.5/4

Measuring and reasoning : = numerical inference in the sciences /
LDR:02907cam a2200241 a 4500 001 1991281
003 DLC
005 20140918153448.0
008 150930s2014 nyuabf b 001 0 eng
010 $a 2013030370
020 $a 9781107024151 $q (hbk.) : $c US75.00
020 $a 1107024153 $q (hbk.)
020 $a 9781139161923 $q (ebk.)
020 $a 113916192X $q (ebk.)
040 $a DLC $b eng
050 0 0 $a QA277 $b .B66 2014
082 0 0 $a 519.5/4 $2 23
100 1 $a Bookstein, Fred L., $d 1947- $3 517369
245 1 0 $a Measuring and reasoning : $b numerical inference in the sciences / $c Fred L. Bookstein.
260 # $a New York : $b Cambridge University Press, $c 2014.
300 $a xxviii, 535 p., [4] p. of plates : $b ill. (some col.), maps (some col.) ; $c 24 cm.
504 $a Includes bibliographical references (p. 501-518) and index.
505 0 # $g The basic structure of a numerical inference -- $t Getting started -- $t Consilience as a rhetorical strategy -- $t Abduction and strong inference -- $g A sampler of strategies -- $t The undergraduate course -- $g Numerical inference for general systems -- $t Abduction and consilience in more complicated systems -- $t The singular value decomposition : a family of pattern engines for organized systems -- $t Morphometrics, and other examples -- $g What is to be done? -- $t Retrospect and prospect.
520 # $a This exploration of empirical inference in science presents a formal description of the process by which scientific measurements support convincing explanations of the world around us"-- $c Provided by publisher.
520 # $a 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 nature 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.
650 # 0 $a Statistical hypothesis testing. $3 560235