An Overview of R Main Concepts Installing R Work Session Help R Objects Functions Packages Exercises Preparing Data Reading Data from File Exporting Results Manipulating Variables Manipulating Individuals Concatenating Data Tables Cross-Tabulation Exercises R Graphics Conventional Graphical Functions Graphical Functions with lattice Exercises Making Programs with R Control Flows Predefined Functions Creating a Function Exercises Statistical Methods Introduction to the Statistical Methods A Quick Start with R Installing R Opening and Closing R The Command Prompt Attribution, Objects, and Function Selection Other Rcmdr Package Importing (or Inputting) Data Graphs Statistical Analysis Hypothesis Test Confidence Intervals for a Mean Chi-Square Test of Independence Comparison of Two Means Testing Conformity of a Proportion Comparing Several Proportions The Power of a Test Regression Simple Linear Regression Multiple Linear Regression Partial Least Squares (PLS) Regression Analysis of Variance and Covariance One-Way Analysis of Variance Multi-Way Analysis of Variance with Interaction Analysis of Covariance Classification Linear Discriminant Analysis Logistic Regression Decision Tree Exploratory Multivariate Analysis Principal Component Analysis Correspondence Analysis Multiple Correspondence Analysis Clustering Ascending Hierarchical Clustering The k-Means Method Appendix The Most Useful Functions Writing a Formula for the Models The Rcmdr Package The FactoMineR Package Answers to the Exercises ck Start with R Installing R Opening and Closing R The Command Prompt Attribution, Objects, and Function Selection Other Rcmdr Package Importing (or Inputting) Data Graphs Statistical Analysis Hypothesis Test Confidence Intervals for a Mean Chi-Square Test of Independence Comparison of Two Means Testing Conformity of a Proportion Comparing Several Proportions The Power of a Test Regression Simple Linear Regression Multiple Linear Regression Partial Least Squares (PLS) Regression Analysis of Variance and Covariance One-Way Analysis of Variance Multi-Way Analysis of Variance with Interaction Analysis of Covariance Classification Linear Discriminant Analysis Logistic Regression Decision Tree Exploratory Multivariate Analysis Principal Component Analysis Correspondence Analysis Multiple Correspondence Analysis Clustering Ascending Hierarchical Clustering The k-Means Method Appendix The Most Useful Functions Writing a Formula for the Models The Rcmdr Package The FactoMineR Package Answers to the Exercises amp;lt;BR>Classification Linear Discriminant Analysis Logistic Regression Decision Tree Exploratory Multivariate Analysis Principal Component Analysis Correspondence Analysis Multiple Correspondence Analysis Clustering Ascending Hierarchical Clustering The k-Means Method Appendix The Most Useful Functions Writing a Formula for the Models The Rcmdr Package The FactoMineR Package Answers to the Exercises.