Advanced R Statistical Programming and Data Models - Analysis, Machine Learning, and Visualization

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 us...

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Bibliographic Details
Main Authors: Wiley, Matt, Wiley, Joshua F.
Format: eBook Book
Language:English
Published: Berkeley, CA Apress, an imprint of Springer Nature 2019
Apress
Apress L. P
Edition:1
Subjects:
Computer programs
Computer Science
Decision making
General Engineering & Project Administration
General References
Industrial management
Mathematical models
Mathematical statistics > Data processing
Probability and Statistics in Computer Science
Professional and Applied Computing
Programming Languages, Compilers, Interpreters
Programming Techniques
R (Computer program language)
Statistical methods
ISBN:9781484228715, 1484228715, 9781484228722, 1484228723
Online Access:Get full text
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Description
Summary: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 this book 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.
Bibliography:Includes bibliographical references and index
ISBN:9781484228715
1484228715
9781484228722
1484228723
DOI:10.1007/978-1-4842-2872-2