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R for statistics
An Overview of RMain ConceptsInstalling RWork SessionHelpR ObjectsFunctionsPackagesExercisesPreparing DataReading Data from FileExporting ResultsManipulating VariablesManipulating IndividualsConcatenating Data TablesCross-TabulationExercisesR GraphicsConventional Graphical FunctionsGraphical Functio...
Autores principales: | , , , , , , , |
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Lenguaje: | eng |
Publicado: |
CRC Press
2012
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Materias: | |
Acceso en línea: | http://cds.cern.ch/record/2295437 |
Sumario: | An Overview of RMain ConceptsInstalling RWork SessionHelpR ObjectsFunctionsPackagesExercisesPreparing DataReading Data from FileExporting ResultsManipulating VariablesManipulating IndividualsConcatenating Data TablesCross-TabulationExercisesR GraphicsConventional Graphical FunctionsGraphical Functions with latticeExercisesMaking Programs with RControl FlowsPredefined FunctionsCreating a FunctionExercisesStatistical MethodsIntroduction to the Statistical MethodsA Quick Start with RInstalling ROpening and Closing RThe Command PromptAttribution, Objects, and FunctionSelectionOther Rcmdr PackageImporting (or Inputting) DataGraphsStatistical AnalysisHypothesis TestConfidence Intervals for a MeanChi-Square Test of IndependenceComparison of Two MeansTesting Conformity of a ProportionComparing Several ProportionsThe Power of a TestRegressionSimple Linear RegressionMultiple Linear RegressionPartial Least Squares (PLS) RegressionAnalysis of Variance and CovarianceOne-Way Analysis of VarianceMulti-Way Analysis of Variance with InteractionAnalysis of CovarianceClassificationLinear Discriminant AnalysisLogistic RegressionDecision TreeExploratory Multivariate AnalysisPrincipal Component AnalysisCorrespondence AnalysisMultiple Correspondence AnalysisClusteringAscending Hierarchical ClusteringThe k-Means MethodAppendixThe Most Useful FunctionsWriting a Formula for the ModelsThe Rcmdr PackageThe FactoMineR PackageAnswers to the Exercises. |
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