Isbn: 9781484228715 - advanced r statistical programming and data models: analysis, machine learning, and visualization (25 risultati)

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Paperback. Condizione: New. 1st ed. Carry out a variety of advanced statistical analyses including generalized additive models, mixed effects models, multiple imputation, machine learning, and missing data techniques using R. Each chapter starts with conceptual background information about the techniques, includes multiple examples using R to achieve results, and concludes with a case study.Written by Matt and Joshua F. Wiley, Advanced R Statistical Programming and Data Models shows you how to conduct data analysis using the popular R language. You'll delve into the preconditions or hypothesis for various statistical tests and techniques and work through concrete examples using R for a variety of these next-level analytics.  This is a must-have guide and reference on using and programming with the R language.  What You'll LearnConduct advanced analyses in R including: generalized linear models, generalized additive models, mixedeffects models, machine learning, and parallel processingCarry out regression modeling using R data visualization, linear and advanced regression, additive models, survival / time to event analysisHandle machine learning using R including parallel processing, dimension reduction, and feature selection and classificationAddress missing data using multiple imputation in RWork on factor analysis, generalized linear mixed models, and modeling intraindividual variability Who This Book Is For Working professionals, researchers, or students who are familiar with R and basic statistical techniques such as linear regression and who want to learn how to use R to perform more advanced analytics. Particularly, researchers and data analysts in the social sciences may benefit from these techniques. Additionally, analysts who need parallel processing to speed up analytics are givenproven code to reduce time to result(s).…

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Paperback. Condizione: New. 1st ed. Carry out a variety of advanced statistical analyses including generalized additive models, mixed effects models, multiple imputation, machine learning, and missing data techniques using R. Each chapter starts with conceptual background information about the techniques, includes multiple examples using R to achieve results, and concludes with a case study.Written by Matt and Joshua F. Wiley, Advanced R Statistical Programming and Data Models shows you how to conduct data analysis using the popular R language. You'll delve into the preconditions or hypothesis for various statistical tests and techniques and work through concrete examples using R for a variety of these next-level analytics.  This is a must-have guide and reference on using and programming with the R language.  What You'll LearnConduct advanced analyses in R including: generalized linear models, generalized additive models, mixedeffects models, machine learning, and parallel processingCarry out regression modeling using R data visualization, linear and advanced regression, additive models, survival / time to event analysisHandle machine learning using R including parallel processing, dimension reduction, and feature selection and classificationAddress missing data using multiple imputation in RWork on factor analysis, generalized linear mixed models, and modeling intraindividual variability Who This Book Is For Working professionals, researchers, or students who are familiar with R and basic statistical techniques such as linear regression and who want to learn how to use R to perform more advanced analytics. Particularly, researchers and data analysts in the social sciences may benefit from these techniques. Additionally, analysts who need parallel processing to speed up analytics are givenproven code to reduce time to result(s).…

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Paperback. Condizione: Brand New. 638 pages. 10.00x7.00x1.50 inches. In Stock.

Paperback. Condizione: New. 1st ed. Carry out a variety of advanced statistical analyses including generalized additive models, mixed effects models, multiple imputation, machine learning, and missing data techniques using R. Each chapter starts with conceptual background information about the techniques, includes multiple examples using R to achieve results, and concludes with a case study.Written by Matt and Joshua F. Wiley, Advanced R Statistical Programming and Data Models shows you how to conduct data analysis using the popular R language. You'll delve into the preconditions or hypothesis for various statistical tests and techniques and work through concrete examples using R for a variety of these next-level analytics.  This is a must-have guide and reference on using and programming with the R language.  What You'll LearnConduct advanced analyses in R including: generalized linear models, generalized additive models, mixedeffects models, machine learning, and parallel processingCarry out regression modeling using R data visualization, linear and advanced regression, additive models, survival / time to event analysisHandle machine learning using R including parallel processing, dimension reduction, and feature selection and classificationAddress missing data using multiple imputation in RWork on factor analysis, generalized linear mixed models, and modeling intraindividual variability Who This Book Is For Working professionals, researchers, or students who are familiar with R and basic statistical techniques such as linear regression and who want to learn how to use R to perform more advanced analytics. Particularly, researchers and data analysts in the social sciences may benefit from these techniques. Additionally, analysts who need parallel processing to speed up analytics are givenproven code to reduce time to result(s).…

Taschenbuch. Condizione: Neu. Advanced R Statistical Programming and Data Models | Analysis, Machine Learning, and Visualization | Matt Wiley (u. a.) | Taschenbuch | xx | Englisch | 2019 | Apress | EAN 9781484228715 | Verantwortliche Person für die EU: APress in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

Paperback. Condizione: New. 1st ed. Carry out a variety of advanced statistical analyses including generalized additive models, mixed effects models, multiple imputation, machine learning, and missing data techniques using R. Each chapter starts with conceptual background information about the techniques, includes multiple examples using R to achieve results, and concludes with a case study.Written by Matt and Joshua F. Wiley, Advanced R Statistical Programming and Data Models shows you how to conduct data analysis using the popular R language. You'll delve into the preconditions or hypothesis for various statistical tests and techniques and work through concrete examples using R for a variety of these next-level analytics.  This is a must-have guide and reference on using and programming with the R language.  What You'll LearnConduct advanced analyses in R including: generalized linear models, generalized additive models, mixedeffects models, machine learning, and parallel processingCarry out regression modeling using R data visualization, linear and advanced regression, additive models, survival / time to event analysisHandle machine learning using R including parallel processing, dimension reduction, and feature selection and classificationAddress missing data using multiple imputation in RWork on factor analysis, generalized linear mixed models, and modeling intraindividual variability Who This Book Is For Working professionals, researchers, or students who are familiar with R and basic statistical techniques such as linear regression and who want to learn how to use R to perform more advanced analytics. Particularly, researchers and data analysts in the social sciences may benefit from these techniques. Additionally, analysts who need parallel processing to speed up analytics are givenproven code to reduce time to result(s).…

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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Carry out a variety of advanced statistical analyses including generalized additive models, mixed effects models, multiple imputation, machine learning, and missing data techniques using R. Each chapter starts with conceptual background information about the techniques, includes multiple examples using R to achieve results, and concludes with a case study.Written by Matt and Joshua F. Wiley, Advanced R Statistical Programming and Data Models shows you how to conduct data analysis using the popular R language. You'll delve into the preconditions or hypothesis for various statistical tests and techniques and work through concrete examples using R for a variety of these next-level analytics. This is a must-have guide and reference on using and programming with the R language. What You'll LearnConduct advanced analyses in R including: generalized linear models, generalized additive models, mixedeffects models, machine learning, and parallel processingCarry out regression modeling using R data visualization, linear and advanced regression, additive models, survival / time to event analysisHandle machine learning using R including parallel processing, dimension reduction, and feature selection and classificationAddress missing data using multiple imputation in RWork on factor analysis, generalized linear mixed models, and modeling intraindividual variabilityWho This Book Is ForWorking professionals, researchers, or students who are familiar with R and basic statistical techniques such as linear regression and who want to learn how to use R to perform more advanced analytics. Particularly, researchers and data analysts in the social sciences may benefit from these techniques. Additionally, analysts who need parallel processing to speed up analytics are givenproven code to reduce time to result(s). 638 pp. Englisch.…

Kartoniert / Broschiert. Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Demonstrates applied R programming to make analyses more efficient and effectiveShows how to handle machine learning using RIncludes case studies throughout bookMa.…