Handbook of statistical analyses using R
Hothorn, Torsten Everitt, Brain S
Handbook of statistical analyses using R - 3 - New York CRC Press 2014 - xxiv,421p. 23cm; Soft
Alpha 1040/02 Jan 15
Rs.4023/-
Table of Contents
Introduction
Density Estimation
Analysis Using R
Summary of Findings
Final Comments
Recursive Partitioning
Introduction
Recursive Partitioning
Analysis Using R
Summary of Findings
Final Comments
Scatterplot Smoothers and Additive Models
Introduction
Scatterplot Smoothers and Generalised Additive Models
Analysis Using R
Summary of Findings
Final Comments
Survival Analysis
Introduction
Survival Analysis
Analysis Using R
Summary of Findings
Final Comments
Quantile Regression
Introduction
Quantile Regression
Analysis Using R
Summary of Findings
Final Comments
Analysing Longitudinal Data I
Introduction
Analysing Longitudinal Data
Linear Mixed Effects Models
Analysis Using R
Prediction of Random Effects
The Problem of Dropouts
Summary of Findings
Final Comments
Analysing Longitudinal Data II
Introduction
Methods for Non-Normal Distributions
Analysis Using R: GEE
Analysis Using R: Random Effects
Summary of Findings
Final Comments
Simultaneous Inference and Multiple Comparisons
Introduction
Simultaneous Inference and Multiple Comparisons
Analysis Using R
Summary of Findings
Final Comments
Missing Values
Introduction
The Problems of Missing Data
Dealing with Missing Values
Imputing Missing Values
Analyzing Multiply Imputed Data
Analysis Using R
Summary of Findings
Final Comments
Meta-Analysis
Introduction
Systematic Reviews and Meta-Analysis
Statistics of Meta-Analysis
Analysis Using R
Meta-Regression
Publication Bias
Summary of Findings
Final Comments
Bayesian Inference
Introduction
Bayesian Inference
Analysis Using R
Summary of Findings
Final Comments
Principal Component Analysis
Introduction
Principal Component Analysis
Analysis Using R
Summary of Findings
Final Comments
Multidimensional Scaling
Introduction
Multidimensional Scaling
Analysis Using R
Summary of Findings
Final Comments
Cluster Analysis
Introduction
Cluster Analysis
Analysis Using R
Summary of Findings
Final Comments
Bibliography
Index
Features
Presents straightforward descriptions of how to use R and interpret the results
Gives an introduction to R for novices, covering basic concepts and common data manipulation techniques
Shows how to obtain informative graphical output by applying the appropriate R functions
Includes more examples and exercises that encourage hands-on practice with R
Offers the data, R code, and lecture slides in the HSAUR3 package available from CRAN
Summary
Like the best-selling first two editions, A Handbook of Statistical Analyses using R, Third Edition provides an up-to-date guide to data analysis using the R system for statistical computing. The book explains how to conduct a range of statistical analyses, from simple inference to recursive partitioning to cluster analysis.
New to the Third Edition
Three new chapters on quantile regression, missing values, and Bayesian inference
Extra material in the logistic regression chapter that describes a regression model for ordered categorical response variables
Additional exercises
More detailed explanations of R code
New section in each chapter summarizing the results of the analyses
Updated version of the HSAUR package (HSAUR3), which includes some slides that can be used in introductory statistics courses
Whether you’re a data analyst, scientist, or student, this handbook shows you how to easily use R to effectively evaluate your data. With numerous real-world examples, it emphasizes the practical application and interpretation of results.
9781482204582
Mathematical Statistics
Data processing handbook
R computer Program Language
R computer Programing Language
519.50285 HOT
Handbook of statistical analyses using R - 3 - New York CRC Press 2014 - xxiv,421p. 23cm; Soft
Alpha 1040/02 Jan 15
Rs.4023/-
Table of Contents
Introduction
Density Estimation
Analysis Using R
Summary of Findings
Final Comments
Recursive Partitioning
Introduction
Recursive Partitioning
Analysis Using R
Summary of Findings
Final Comments
Scatterplot Smoothers and Additive Models
Introduction
Scatterplot Smoothers and Generalised Additive Models
Analysis Using R
Summary of Findings
Final Comments
Survival Analysis
Introduction
Survival Analysis
Analysis Using R
Summary of Findings
Final Comments
Quantile Regression
Introduction
Quantile Regression
Analysis Using R
Summary of Findings
Final Comments
Analysing Longitudinal Data I
Introduction
Analysing Longitudinal Data
Linear Mixed Effects Models
Analysis Using R
Prediction of Random Effects
The Problem of Dropouts
Summary of Findings
Final Comments
Analysing Longitudinal Data II
Introduction
Methods for Non-Normal Distributions
Analysis Using R: GEE
Analysis Using R: Random Effects
Summary of Findings
Final Comments
Simultaneous Inference and Multiple Comparisons
Introduction
Simultaneous Inference and Multiple Comparisons
Analysis Using R
Summary of Findings
Final Comments
Missing Values
Introduction
The Problems of Missing Data
Dealing with Missing Values
Imputing Missing Values
Analyzing Multiply Imputed Data
Analysis Using R
Summary of Findings
Final Comments
Meta-Analysis
Introduction
Systematic Reviews and Meta-Analysis
Statistics of Meta-Analysis
Analysis Using R
Meta-Regression
Publication Bias
Summary of Findings
Final Comments
Bayesian Inference
Introduction
Bayesian Inference
Analysis Using R
Summary of Findings
Final Comments
Principal Component Analysis
Introduction
Principal Component Analysis
Analysis Using R
Summary of Findings
Final Comments
Multidimensional Scaling
Introduction
Multidimensional Scaling
Analysis Using R
Summary of Findings
Final Comments
Cluster Analysis
Introduction
Cluster Analysis
Analysis Using R
Summary of Findings
Final Comments
Bibliography
Index
Features
Presents straightforward descriptions of how to use R and interpret the results
Gives an introduction to R for novices, covering basic concepts and common data manipulation techniques
Shows how to obtain informative graphical output by applying the appropriate R functions
Includes more examples and exercises that encourage hands-on practice with R
Offers the data, R code, and lecture slides in the HSAUR3 package available from CRAN
Summary
Like the best-selling first two editions, A Handbook of Statistical Analyses using R, Third Edition provides an up-to-date guide to data analysis using the R system for statistical computing. The book explains how to conduct a range of statistical analyses, from simple inference to recursive partitioning to cluster analysis.
New to the Third Edition
Three new chapters on quantile regression, missing values, and Bayesian inference
Extra material in the logistic regression chapter that describes a regression model for ordered categorical response variables
Additional exercises
More detailed explanations of R code
New section in each chapter summarizing the results of the analyses
Updated version of the HSAUR package (HSAUR3), which includes some slides that can be used in introductory statistics courses
Whether you’re a data analyst, scientist, or student, this handbook shows you how to easily use R to effectively evaluate your data. With numerous real-world examples, it emphasizes the practical application and interpretation of results.
9781482204582
Mathematical Statistics
Data processing handbook
R computer Program Language
R computer Programing Language
519.50285 HOT