LEADER 06250nam 22007575 450 001 996490346003316 005 20240221154703.0 010 $a3-031-07566-8 024 7 $a10.1007/978-3-031-07566-7 035 $a(MiAaPQ)EBC7098150 035 $a(Au-PeEL)EBL7098150 035 $a(CKB)24866038600041 035 $a(DE-He213)978-3-031-07566-7 035 $a(PPN)264957733 035 $a(EXLCZ)9924866038600041 100 $a20220920d2022 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aModern Statistics$b[electronic resource] $eA Computer-Based Approach with Python /$fby Ron S. Kenett, Shelemyahu Zacks, Peter Gedeck 205 $a1st ed. 2022. 210 1$aCham :$cSpringer International Publishing :$cImprint: Birkhäuser,$d2022. 215 $a1 online resource (453 pages) 225 1 $aStatistics for Industry, Technology, and Engineering,$x2662-5563 311 08$aPrint version: Kenett, Ron. S Modern Statistics Cham : Springer International Publishing AG,c2022 9783031075650 327 $aAnalyzing Variability: Descriptive Statistics -- Probability Models and Distribution Functions -- Statistical Inference and Bootstrapping -- Variability in Several Dimensions and Regression Models -- Sampling for Estimation of Finite Population Quantities -- Time Series Analysis and Prediction -- Modern analytic methods: Part I -- Modern analytic methods: Part II -- Introduction to Python -- List of Python packages -- Code Repository and Solution Manual -- Bibliography -- Index. 330 $aThis innovative textbook presents material for a course on modern statistics that incorporates Python as a pedagogical and practical resource. Drawing on many years of teaching and conducting research in various applied and industrial settings, the authors have carefully tailored the text to provide an ideal balance of theory and practical applications. Numerous examples and case studies are incorporated throughout, and comprehensive Python applications are illustrated in detail. A custom Python package is available for download, allowing students to reproduce these examples and explore others. The first chapters of the text focus on analyzing variability, probability models, and distribution functions. Next, the authors introduce statistical inference and bootstrapping, and variability in several dimensions and regression models. The text then goes on to cover sampling for estimation of finite population quantities and time series analysis and prediction, concluding with two chapters on modern data analytic methods. Each chapter includes exercises, data sets, and applications to supplement learning. Modern Statistics: A Computer-Based Approach with Python is intended for a one- or two-semester advanced undergraduate or graduate course. Because of the foundational nature of the text, it can be combined with any program requiring data analysis in its curriculum, such as courses on data science, industrial statistics, physical and social sciences, and engineering. Researchers, practitioners, and data scientists will also find it to be a useful resource with the numerous applications and case studies that are included. A second, closely related textbook is titled Industrial Statistics: A Computer-Based Approach with Python. It covers topics such as statistical process control, including multivariate methods, the design of experiments, including computer experiments and reliability methods, including Bayesian reliability. These texts can be used independently or for consecutive courses The mistat Python package can be accessed at https://gedeck.github.io/mistat-code-solutions/ModernStatistics/ "In this book on Modern Statistics, the last two chapters on modern analytic methods contain what is very popular at the moment, especially in Machine Learning, such as classifiers, clustering methods and text analytics. But I also appreciate the previous chapters since I believe that people using machine learning methods should be aware that they rely heavily on statistical ones. I very much appreciate the many worked out cases, based on the longstanding experience of the authors. They are very useful to better understand, and then apply, the methods presented in the book. The use of Python corresponds to the best programming experience nowadays. For all these reasons, I think the book has also a brilliant and impactful future and I commend the authors for that." Professor Fabrizio Ruggeri Research Director at the National Research Council, Italy President of the International Society for Business and Industrial Statistics (ISBIS) Editor-in-Chief of Applied Stochastic Models in Business and Industry (ASMBI) . 410 0$aStatistics for Industry, Technology, and Engineering,$x2662-5563 606 $aMathematical statistics$xData processing 606 $aStatistics 606 $aArtificial intelligence$xData processing 606 $aIndustrial engineering 606 $aProduction engineering 606 $aStatistics and Computing 606 $aStatistical Theory and Methods 606 $aData Science 606 $aIndustrial and Production Engineering 606 $aEstadística$2thub 606 $aProcessament de dades$2thub 606 $aPython (Llenguatge de programació)$2thub 608 $aLlibres electrònics$2thub 615 0$aMathematical statistics$xData processing. 615 0$aStatistics. 615 0$aArtificial intelligence$xData processing. 615 0$aIndustrial engineering. 615 0$aProduction engineering. 615 14$aStatistics and Computing. 615 24$aStatistical Theory and Methods. 615 24$aData Science. 615 24$aIndustrial and Production Engineering. 615 7$aEstadística 615 7$aProcessament de dades 615 7$aPython (Llenguatge de programació) 676 $a005.133 700 $aKenett$b Ron S.$0117697 702 $aZacks$b Shelemyahu 702 $aGedeck$b Peter 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996490346003316 996 $aModern statistics$93027978 997 $aUNISA