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Modern Statistics : A Computer Based Approach With Python Engineering
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N.º de artículo de eBay:336017149829
Última actualización el 06 oct 2025 13:17:17 H.EspVer todas las actualizacionesVer todas las actualizaciones
Características del artículo
- Estado
- ISBN
- 9783031075650
Acerca de este producto
Product Identifiers
Publisher
Springer International Publishing A&G
ISBN-10
303107565X
ISBN-13
9783031075650
eBay Product ID (ePID)
20057285995
Product Key Features
Number of Pages
Xxiii, 438 Pages
Language
English
Publication Name
Modern Statistics : a Computer-Based Approach with Python
Publication Year
2022
Subject
Mathematical & Statistical Software, Probability & Statistics / General, General, Databases / General
Type
Textbook
Subject Area
Mathematics, Computers
Series
Statistics for Industry, Technology, and Engineering Ser.
Format
Hardcover
Dimensions
Item Weight
30.2 Oz
Item Length
9.3 in
Item Width
6.1 in
Additional Product Features
Dewey Edition
23
Number of Volumes
1 vol.
Illustrated
Yes
Dewey Decimal
519.502855133
Table Of Content
Analyzing 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.
Synopsis
This 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 computerexperiments 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 thinkthe book has also a brilliant and impactful future and I commend the authors for that." Professor Fabrizio RuggeriResearch Director at the National Research Council, ItalyPresident of the International Society for Business and Industrial Statistics (ISBIS)Editor-in-Chief of Applied Stochastic Models in Business and Industry (ASMBI), This 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 computerexperiments 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 thinkthe 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)
LC Classification Number
QA276.4-.45
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100% de votos positivos•5,2 mil artículos vendidos
Registrado como vendedor particularPor tanto, no se aplican los derechos de los consumidores derivados de las leyes de protección de los consumidores de la UE. La Garantía al cliente de eBay sigue aplicando a la mayoría de compras. Más informaciónMás información
Votos de vendedor (2.143)
- l***4 (3255)- Votos emitidos por el comprador.Últimos 6 mesesCompra verificadaThese items were mailed promptly, arrived by the anticipated delivery date, and were exactly as described. Items were securely packaged and, as a result, were undamaged during shipment. Buyer appreciated the conscientiousness and communication from the seller. This was a smooth transaction, and I had a nice experience working with this seller. Thank you for making these items available.
- l***d (1317)- Votos emitidos por el comprador.Último añoCompra verificadaAuthentic seller. Item as described, was priced very reasonably, was put in a tight box with neat cushions and taped very tightly and securely with good tapes leaving no room for damage and shipped fast. Excellent communication with me. Very happy with purchase and seller!Calculus : Early Transcendentals Eighth Edition by James Stewart Hardcover (#335799520609)
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