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R for Statistics

R for Statistics

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  • ISBN:9781439881453
  • Editorial: Crc Press Inc.
  • Fecha de la edición:
  • Encuadernación:
  • Nº Pág.:320
  • Idiomas:Español

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Although there are currently a wide variety of software packages suitable for the modern statistician, R has the triple advantage of being comprehensive, widespread, and free. Published in 2008, the second edition of Statistiques avec R enjoyed great success as an R guidebook in the French-speaking world. Translated and updated, R for Statistics includes a number of expanded and additional worked examples. Organized into two sections, the book focuses first on the R software, then on the implementation of traditional statistical methods with R. Focusing on the R software, the first section covers: Basic elements of the R software and data processing Clear, concise visualization of results, using simple and complex graphs Programming basics: pre-defined and user-created functions The second section of the book presents R methods for a wide range of traditional statistical data processing techniques, including: Regression methods Analyses of variance and covariance Classification methods Exploratory multivariate analysis Clustering methods Hypothesis tests After a short presentation of the method, the book explicitly details the R command lines and gives commented results. Accessible to novices and experts alike, R for Statistics is a clear and enjoyable resource for any scientist.

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

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