國立高雄大學圖資館 |

Language: English

Back

Statistical design and analysis of b...

Kaltenbach, Hans-Michael.

Statistical design and analysis of biological experiments

Record Type:	Electronic resources : Monograph/item
Title/Author:	Statistical design and analysis of biological experimentsby Hans-Michael Kaltenbach.
Author:	Kaltenbach, Hans-Michael.
Published:	Cham :Springer International Publishing :2021.
Description:	xiv, 269 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Experimental design.
Online resource:	https://doi.org/10.1007/978-3-030-69641-2
ISBN:	9783030696412$q(electronic bk.)

Statistical design and analysis of biological experiments
Kaltenbach, Hans-Michael.

Statistical design and analysis of biological experiments[electronic resource] /by Hans-Michael Kaltenbach. - Cham :Springer International Publishing :2021. - xiv, 269 p. :ill., digital ;24 cm. - Statistics for biology and health,1431-8776. - Statistics for biology and health..

Principles of Experimental Design -- Review of Statistical Concepts -- Planning for Precision and Power -- Comparing More than Two Groups -- Comparing Treatment Groups with Linear Contrasts -- Multiple Treatment Factors: Factorial Designs -- Improving Precision and Power: Blocked Designs -- Split-Unit Designs -- Many Treatment Factors: Fractional Factorial Designs -- Experimental Optimization with Response Surface Methods -- References -- Index.

This richly illustrated book provides an overview of the design and analysis of experiments with a focus on non-clinical experiments in the life sciences, including animal research. It covers the most common aspects of experimental design such as handling multiple treatment factors and improving precision. In addition, it addresses experiments with large numbers of treatment factors and response surface methods for optimizing experimental conditions or biotechnological yields. The book emphasizes the estimation of effect sizes and the principled use of statistical arguments in the broader scientific context. It gradually transitions from classical analysis of variance to modern linear mixed models, and provides detailed information on power analysis and sample size determination, including 'portable power' formulas for making quick approximate calculations. In turn, detailed discussions of several real-life examples illustrate the complexities and aberrations that can arise in practice. Chiefly intended for students, teachers and researchers in the fields of experimental biology and biomedicine, the book is largely self-contained and starts with the necessary background on basic statistical concepts. The underlying ideas and necessary mathematics are gradually introduced in increasingly complex variants of a single example. Hasse diagrams serve as a powerful method for visualizing and comparing experimental designs and deriving appropriate models for their analysis. Manual calculations are provided for early examples, allowing the reader to follow the analyses in detail. More complex calculations rely on the statistical software R, but are easily transferable to other software. Though there are few prerequisites for effectively using the book, previous exposure to basic statistical ideas and the software R would be advisable.

ISBN: 9783030696412$q(electronic bk.)

Standard No.: 10.1007/978-3-030-69641-2doiSubjects--Topical Terms:

181887
Experimental design.

LC Class. No.: QH316.5 / .K358 2021

Dewey Class. No.: 570.727

Statistical design and analysis of biological experiments
LDR:03364nmm a2200337 a 4500 001 597976
003 DE-He213
005 20210730101233.0
006 m d
007 cr nn 008maaau
008 211019s2021 sz s 0 eng d
020 $a 9783030696412$q(electronic bk.)
020 $a 9783030696405$q(paper)
024 7 $a 10.1007/978-3-030-69641-2 $2 doi
035 $a 978-3-030-69641-2
040 $a GP $c GP
041 0 $a eng
050 4 $a QH316.5 $b .K358 2021
072 7 $a PBT $2 bicssc
072 7 $a MAT029000 $2 bisacsh
072 7 $a PBT $2 thema
082 0 4 $a 570.727 $2 23
090 $a QH316.5 $b .K14 2021
100 1 $a Kaltenbach, Hans-Michael. $3 557770
245 1 0 $a Statistical design and analysis of biological experiments $h [electronic resource] / $c by Hans-Michael Kaltenbach.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a xiv, 269 p. : $b ill., digital ; $c 24 cm.
490 1 $a Statistics for biology and health, $x 1431-8776
505 0 $a Principles of Experimental Design -- Review of Statistical Concepts -- Planning for Precision and Power -- Comparing More than Two Groups -- Comparing Treatment Groups with Linear Contrasts -- Multiple Treatment Factors: Factorial Designs -- Improving Precision and Power: Blocked Designs -- Split-Unit Designs -- Many Treatment Factors: Fractional Factorial Designs -- Experimental Optimization with Response Surface Methods -- References -- Index.
520 $a This richly illustrated book provides an overview of the design and analysis of experiments with a focus on non-clinical experiments in the life sciences, including animal research. It covers the most common aspects of experimental design such as handling multiple treatment factors and improving precision. In addition, it addresses experiments with large numbers of treatment factors and response surface methods for optimizing experimental conditions or biotechnological yields. The book emphasizes the estimation of effect sizes and the principled use of statistical arguments in the broader scientific context. It gradually transitions from classical analysis of variance to modern linear mixed models, and provides detailed information on power analysis and sample size determination, including 'portable power' formulas for making quick approximate calculations. In turn, detailed discussions of several real-life examples illustrate the complexities and aberrations that can arise in practice. Chiefly intended for students, teachers and researchers in the fields of experimental biology and biomedicine, the book is largely self-contained and starts with the necessary background on basic statistical concepts. The underlying ideas and necessary mathematics are gradually introduced in increasingly complex variants of a single example. Hasse diagrams serve as a powerful method for visualizing and comparing experimental designs and deriving appropriate models for their analysis. Manual calculations are provided for early examples, allowing the reader to follow the analyses in detail. More complex calculations rely on the statistical software R, but are easily transferable to other software. Though there are few prerequisites for effectively using the book, previous exposure to basic statistical ideas and the software R would be advisable.
650 0 $a Experimental design. $3 181887
650 0 $a Biology $x Experiments $x Statistical methods. $3 891499
650 1 4 $a Statistical Theory and Methods. $3 274054
650 2 4 $a Bioinformatics. $3 194415
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a Statistics for biology and health. $3 558271
856 4 0 $u https://doi.org/10.1007/978-3-030-69641-2
950 $a Mathematics and Statistics (SpringerNature-11649)