LEADER 04004nam 22005655 450 001 9910484898003321 005 20250318140741.0 010 $a9783030696412 010 $a3030696413 024 7 $a10.1007/978-3-030-69641-2 035 $a(CKB)4100000011891447 035 $a(MiAaPQ)EBC6552123 035 $a(Au-PeEL)EBL6552123 035 $a(OCoLC)1246445644 035 $a(PPN)255290020 035 $a(DE-He213)978-3-030-69641-2 035 $a(EXLCZ)994100000011891447 100 $a20210415d2021 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aStatistical Design and Analysis of Biological Experiments /$fby Hans-Michael Kaltenbach 205 $a1st ed. 2021. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2021. 215 $a1 online resource (281 pages) 225 1 $aStatistics for Biology and Health,$x2197-5671 311 08$a9783030696405 311 08$a3030696405 327 $aPrinciples 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. 330 $aThis richly illustrated book provides an overview of the design and analysis of experiments with a focus on non-clinical experiments in the life sciences, including animal research. It covers the most common aspects of experimental design such as handling multiple treatment factors and improving precision. In addition, it addresses experiments with large numbers of treatment factors and response surface methods for optimizing experimental conditions or biotechnological yields. The book emphasizes the estimation of effect sizes and the principled use of statistical arguments in the broader scientific context. It gradually transitions from classical analysis of variance to modern linear mixed models, and provides detailed information on power analysis and sample size determination, including ?portable power? formulas for making quick approximate calculations. In turn, detailed discussions of several real-life examples illustrate the complexities and aberrations that can arise in practice. Chiefly intended for students, teachers and researchers in the fields of experimental biology and biomedicine, the book is largely self-contained and starts with the necessary background on basic statistical concepts. The underlying ideas and necessary mathematics are gradually introduced in increasingly complex variants of a single example. Hasse diagrams serve as a powerful method for visualizing and comparing experimental designs and deriving appropriate models for their analysis. Manual calculations are provided for early examples, allowing the reader to follow the analyses in detail. More complex calculations rely on the statistical software R, but are easily transferable to other software. Though there are few prerequisites for effectively using the book, previous exposure to basic statistical ideas and the software R would be advisable. 410 0$aStatistics for Biology and Health,$x2197-5671 606 $aStatistics 606 $aBioinformatics 606 $aStatistical Theory and Methods 606 $aBioinformatics 615 0$aStatistics. 615 0$aBioinformatics. 615 14$aStatistical Theory and Methods. 615 24$aBioinformatics. 676 $a001.434 700 $aKaltenbach$b Hans-Michael$0852023 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910484898003321 996 $aStatistical design and analysis of biological experiments$91902420 997 $aUNINA