Chapman and Hall/CRC
The R Companion to Elementary Applied Statistics (PDF) includes traditional applications covered in elementary statistics courses as well as some additional methods that address questions that might arise during or after the application of commonly used methods. Beginning with basic tasks and computations with R; readers are then guided through ways to bring data into R; manipulate the data as needed; perform common statistical computations and elementary exploratory data analysis tasks; prepare customized graphics; and take advantage of R for a wide range of methods that find use in many elementary applications of statistics.
- Requires no familiarity with R or programming to begin using this ebook.
- Presents quite a few methods that may be considered non-traditional; or advanced.
- Includes accompanying carefully documented script files that contain code for all examples presented
- It contains an extensive array of examples that illustrate ideas on various ways to use pre-packaged routines; as well as on developing individualized code.
- It can be used as a resource for a project-based elementary applied statistics course; or for researchers and professionals who wish to delve more deeply into R.
R is a powerful and free product that is gaining popularity across the scientific community in both the professional and academic arenas. Statistical methods discussed in this ebook are used to introduce the fundamentals of using R functions and provide ideas for developing further skills in writing R code. These ideas are illustrated through an extensive collection of examples.
NOTE: This product only includes the ebook R Companion to Elementary Applied Statistics in PDF. No access codes included.
Table of contents
Table of contents :
Content: PreliminariesFirst StepsRunning Code in RSome TerminologyHierarchy of Data ClassesData StructuresOperatorsFunctionsR PackagesProbability DistributionsCoding ConventionsSome Book-keeping and Other TipsGetting Quick Coding HelpBringing Data Into and Out of REntering Data Through CodingNumber and Sample Generating TricksThe R Data EditorReading Text FilesReading Data from Other File Formats Reading Data from the Keyboard Saving and Exporting DataAccessing Contents of Data StructuresExtracting Data from VectorsConducting Data Searches in VectorsWorking with FactorsNavigating Data FramesListsChoosing an Access/Extraction MethodAdditional NotesMore About the attach FunctionAbout Functions and their ArgumentsAlternative Argument Assignments in Function Calls Altering and Manipulating DataAltering Entries in Vectors TransformationsManipulating Character StringsSorting Vectors and FactorsAltering Data Frames Sorting Data Frames Moving Between Lists and Data FramesAdditional Notes on the merge FunctionSummaries and StatisticsUnivariate Frequency Distributions Bivariate Frequency DistributionsStatistics for Univariate Samples Measures of Central TendencyMeasures of SpreadMeasures of PositionMeasures of ShapeFive-Number Summaries and OutliersElementary Five-Number SummaryTukey’s Five-Number The boxplotstats FunctionMore on Computing with RComputing with Numeric VectorsWorking with Lists, Data Frames and ArraysThe sapply FunctionThe tapply FunctionThe by FunctionThe aggregate Function The apply FunctionThe sweep FunctionFor-loops Conditional Statements and the switch FunctionThe if-then Statement The if-then-else StatementThe switch Function Preparing Your Own FunctionsBasic Charts for Categorical DataPreliminary Comments Bar Charts Dot Charts Pie ChartsExporting Graphics Images Additional Notes Customizing Plotting Windows The plotnew and plotwindow FunctionsMore on the paste Function The title FunctionMore on the legend FunctionMore on the mtext FunctionThe text FunctionBasic Plots for Numeric DataHistogramsBoxplotsStripcharts QQ-PlotsNormal Probability QQ-PlotsInterpreting Normal Probability QQ-PlotsMore on Reference Lines for QQ-Plots QQ-Plots for Other DistributionsAdditional NotesMore on the ifelse Function Revisiting the axis FunctionFrequency Polygons and OgivesScatterplots, Lines, and Curves ScatterplotsBasic PlotsManipulating Plotting CharactersPlotting Transformed DataMatrix ScatterplotsThe matplot FunctionGraphs of LinesGraphs of CurvesSuperimposing Multiple Lines and/or CurvesTime-series PlotsMore Graphics ToolsPartitioning Graphics Windows The layout FunctionThe splitscreen Function Customizing Plotted Text and Symbols Inserting Mathematical Annotation in PlotsMore Low-level Graphics Functions The points and symbols FunctionsThe grid, segments and arrows FunctionsBoxes, Rectangles and PolygonsError BarsComputing Bounds for Error BarsThe errorBarplot Function Purpose and Interpretation of Error BarsMore R Graphics Resources Tests for One and Two ProportionsRelevant Probability DistributionsBinomial DistributionsHypergeometric DistributionsNormal DistributionsChi-square DistributionsSingle Population ProportionsEstimating a Population ProportionHypotheses for Single Proportion TestsA Normal Approximation TestA Chi-square TestAn Exact TestWhich Approach Should be Used?Two Population ProportionsEstimating Differences Between ProportionsHypotheses for Two Proportions Tests A Normal Approximation TestA Chi-square TestFisher’s Exact TestWhich Approach Should be Used?Additional Notes Normal Approximations of Binomial Distributions One- versus Two-sided Hypothesis Tests Tests for More than Two ProportionsEquality of Three or More ProportionsPearson’s Homogeneity of Proportions Test Marascuilo’s Large Sample Procedure Cohen’s Small Sample Procedure Simultaneous Pairwise Comparisons Marascuilo’s Large Sample Procedure Cohen’s Small Sample Procedure Linear Contrasts of Proportions Marascuilo’s Large Sample ApproachCohen’s Small Sample ApproachThe Chi-square Goodness-of-Fit Test Tests of Variances and SpreadRelevant Probability DistributionsF Distributions Using a Sample to Assess Normality Single Population VariancesEstimating a Variance Testing a VarianceExactly Two Population VariancesEstimating the Ratio of Two Variances Testing the Ratio of Two Variances What if the Normality Assumption is Violated?Two or More Population VariancesAssessing Spread GraphicallyLevene’s Test Levene’s Test with Trimmed MeansBrown-Forsythe TestFligner-Killeen Test Tests for One or Two MeansStudent’s t-Distribution Single Population MeansVerifying the Normality Assumption Estimating a MeanTesting a MeanCan a Normal Approximation be Used Here? Exactly Two Population MeansVerifying AssumptionsThe Test for Dependent SamplesTests for Independent Samples Tests for More than Two MeansRelevant Probability DistributionsStudentized Range DistributionDunnett’s Test DistributionStudentized Maximum Modulus DistributionSetting the StageEquality of Means – Equal Variances CasePairwise Comparisons – Equal VariancesBonferroni’s ProcedureTukey’s Procedure t Tests and Comparisons with a ControlDunnett’s Test and Comparisons with a ControlWhich Procedure to Choose Equality of Means – Unequal Variances CaseLarge-sample Chi-square TestWelch’s F TestHotelling’s T TestPairwise Comparisons – Unequal VariancesLarge-sample Chi-square Test Dunnett’s C Procedure Dunnett’s T ProcedureComparisons with a ControlWhich Procedure to ChooseThe Nature of Differences Found All Possible Pairwise Comparisons Comparisons with a ControlSelected Tests for Medians, and MoreRelevant Probability Distributions Distribution of the Signed Rank Statistic Distribution of the Rank Sum Statistic The One-sample Sign TestThe Exact Test The Normal Approximation Paired Samples Sign TestIndependent Samples Median Test Equality of Medians Pairwise Comparisons of MediansSingle Sample Signed Rank TestThe Exact Test The Normal ApproximationPaired Samples Signed Rank Test Rank Sum Test of Medians The Exact Mann-Whitney TestThe Normal Approximation The Wilcoxon Rank Sum Test Using the Kruskal-Wallis Test to Test MediansWorking with Ordinal DataPaired Samples Independent Samples More than Two Independent Samples Some Comments on the Use of Ordinal DataDependence and IndependenceAssessing Bivariate NormalityPearson’s Correlation Coefficient An Interval Estimate of Testing the Significance of Testing a Null Hypothesis with Kendall’s Correlation CoefficientAn Interval Estimate of Exact Test of the Significance of Approximate Test of the Significance of Spearman’s Rank Correlation CoefficientExact Test of the Significance of S Approximate Test of the Significance SCorrelations in General – Comments and CautionsChi-square Test of IndependenceFor the Curious – Distributions of rK and rS