LEADER 04383nam 22005895 450 001 9910483429503321 005 20251202165743.0 010 $a3-030-43328-5 024 7 $a10.1007/978-3-030-43328-4 035 $a(CKB)4100000011273835 035 $a(MiAaPQ)EBC6191433 035 $a(DE-He213)978-3-030-43328-4 035 $a(PPN)248397575 035 $a(MiAaPQ)EBC6191380 035 $a(MiAaPQ)EBC29093087 035 $a(EXLCZ)994100000011273835 100 $a20200504d2020 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aStatistical Analysis of Empirical Data $eMethods for Applied Sciences /$fby Scott Pardo 205 $a1st ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (278 pages) 311 08$a3-030-43327-7 327 $aChapter 1: Fundamentals -- Chapter 2: Sample Statistics are NOT Parameters -- Chapter 3: Confidence -- Chapter 4: Multiplicity and Multiple Comparisons -- Chapter 5: Power and the Myth of Sample Size Determination -- Chapter 6: Regression and Model Fitting with Collinearity -- Chapter 7: Overparameterization -- Chapter 8: Ignoring Error Control Factors and Experimental Design -- Chapter 9: Generalized Linear Models -- Chapter 10: Mixed Models and Variance Components -- Chapter 11: Models, Models Everywhere...Model Selection -- Chapter 12: Bayesian Analyses -- Chapter 13: The Acceptance Sampling Game -- Chapter 14: Nonparametric Statistics - A Strange Name -- Chapter 15: Autocorrelated Data and Dynamic Systems -- Chapter 16: Multivariate Analysis and Classification -- Chapter 17: Time-to-Event: Survival and Life Testing -- Index. 330 $aResearchers and students who use empirical investigation in their work must go through the process of selecting statistical methods for analyses, and they are often challenged to justify these selections. This book is designed for readers with limited background in statistical methodology who seek guidance in defending their statistical decision-making in the worlds of research and practice. It is devoted to helping students and scholars find the information they need to select data analytic methods, and to speak knowledgeably about their statistical research processes. Each chapter opens with a conundrum relating to the selection of an analysis, or to explaining the nature of an analysis. Throughout the chapter, the analysis is described, along with some guidance in justifying the choices of that particular method. Designed to offer statistical knowledge to the non-specialist, this volume can be used in courses on research methods, or for courses on statistical applications to biological, medical, life, social, or physical sciences. It will also be useful to academic and industrial researchers in engineering and in the physical sciences who will benefit from a stronger understanding of how to analyze empirical data. The book is written for those with foundational education in calculus. However, a brief review of fundamental concepts of probability and statistics, together with a primer on some concepts in elementary calculus and matrix algebra, is included. R code and sample datasets are provided. 606 $aStatistics 606 $aBiometry 606 $aSocial sciences$xStatistical methods 606 $aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences 606 $aBayesian Inference 606 $aBiostatistics 606 $aStatistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy 615 0$aStatistics. 615 0$aBiometry. 615 0$aSocial sciences$xStatistical methods. 615 14$aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences. 615 24$aBayesian Inference. 615 24$aBiostatistics. 615 24$aStatistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy. 676 $a519.5 700 $aPardo$b Scott$4aut$4http://id.loc.gov/vocabulary/relators/aut$0856379 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910483429503321 996 $aStatistical Analysis of Empirical Data$91912502 997 $aUNINA