- Advanced R Statistical Programming and Data Models
- Matt Wiley
- 05 August 2020
Matt Wiley ê 9 review
Advanced R Statistical Programming and Data Models Read & Download ☆ E-book, or Kindle E-pub Carry out a variety of advanced statistical analyses including generalized additive models mixed effects models multiple imputation machine learning and missing data techniues using R Each chapter starts with conceptual background information about the techniues includes multiple examples using R to achieve results and concludes with a case studyWritten 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 techniu.Read Advanced R Statistical Programming and Data Models
Advanced R Statistical Programming and Data Models Read & Download ☆ E-book, or Kindle E-pub AssificationAddress 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 techniues such as linear regression and who want to learn how to use R to perform advanced analytics Particularly researchers and data analysts in the social sciences may benefit from these techniues Additionally analysts who need parallel processing to speed up analytics are given proven code to reduce time to results.
Free download Å E-book, or Kindle E-pub ê Matt Wiley
Advanced R Statistical Programming and Data Models Read & Download ☆ E-book, or Kindle E-pub Es 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 mixed effects 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 cl.