001449834 000__ 05763cam\a2200553\i\4500
001449834 001__ 1449834
001449834 003__ OCoLC
001449834 005__ 20230310004421.0
001449834 006__ m\\\\\o\\d\\\\\\\\
001449834 007__ cr\cn\nnnunnun
001449834 008__ 220927s2022\\\\sz\a\\\\ob\\\\001\0\eng\d
001449834 019__ $$a1345582403
001449834 020__ $$a9783031075667$$q(electronic bk.)
001449834 020__ $$a3031075668$$q(electronic bk.)
001449834 020__ $$z9783031075650
001449834 020__ $$z303107565X
001449834 0247_ $$a10.1007/978-3-031-07566-7$$2doi
001449834 035__ $$aSP(OCoLC)1346096755
001449834 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dYDX$$dOCLCQ
001449834 049__ $$aISEA
001449834 050_4 $$aQA276.45.P9
001449834 08204 $$a519.50285/5133$$223/eng/20220927
001449834 1001_ $$aKenett, Ron,$$eauthor.$$1https://isni.org/isni/0000000078753871
001449834 24510 $$aModern statistics :$$ba computer-based approach with Python /$$cRon Kenett, Shelemyahu Zacks, Peter Gedeck.
001449834 264_1 $$aCham :$$bBirkhäuser,$$c[2022]
001449834 264_4 $$c©2022
001449834 300__ $$a1 online resource (xxiii, 438 pages) :$$billustrations (some color).
001449834 336__ $$atext$$btxt$$2rdacontent
001449834 337__ $$acomputer$$bc$$2rdamedia
001449834 338__ $$aonline resource$$bcr$$2rdacarrier
001449834 4901_ $$aStatistics for industry, technology, and engineering
001449834 504__ $$aIncludes bibliographical references and index.
001449834 5050_ $$aAnalyzing Variability: Descriptive Statistics -- Probability Models and Distribution Functions -- Statistical Inference and Bootstrapping -- Variability in Several Dimensions and Regression Models -- Sampling for Estimation of Finite Population Quantities -- Time Series Analysis and Prediction -- Modern analytic methods: Part I -- Modern analytic methods: Part II -- Introduction to Python -- List of Python packages -- Code Repository and Solution Manual -- Bibliography -- Index.
001449834 506__ $$aAccess limited to authorized users.
001449834 520__ $$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) .
001449834 588__ $$aDescription based on print version record.
001449834 650_0 $$aStatistics$$xData processing.
001449834 650_0 $$aPython (Computer program language)
001449834 655_0 $$aElectronic books.
001449834 7001_ $$aZacks, Shelemyahu,$$d1932-$$eauthor.$$1https://isni.org/isni/000000010925307X
001449834 7001_ $$aGedeck, Peter,$$eauthor.$$1https://isni.org/isni/0000000500686310
001449834 77608 $$iPrint version:$$aKenett, Ron.$$tModern statistics.$$dCham : Springer, 2022$$z9783031075650$$w(OCoLC)1338680133
001449834 830_0 $$aStatistics for industry, technology, and engineering.
001449834 852__ $$bebk
001449834 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-07566-7$$zOnline Access$$91397441.1
001449834 909CO $$ooai:library.usi.edu:1449834$$pGLOBAL_SET
001449834 980__ $$aBIB
001449834 980__ $$aEBOOK
001449834 982__ $$aEbook
001449834 983__ $$aOnline
001449834 994__ $$a92$$bISE