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.

Contiene: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.