Statistics and Data Visualization Using R

David Brown is a Professor and Divisional Dean of Social Sciences at the University of Colorado Boulder.

This book provides a well-written approach to beginning to intermediate-level statistical principles using the R statistical language. It provides some mathematical formulas to help students understand the underlying principles of statistics. It has many excellent social science examples. It provides the statistical understanding with a practical approach to using the most valuable statistical tool—R. Please consider it. I have been looking for a good social science textbook using R—this may be the best so far.

• PrefaceAcknowledgmentsAbout the AuthorChapter 1: Getting StartedLearning ObjectivesOverviewR, RStudio, and R MarkdownObjects and FunctionsGetting Started in RStudioNavigating RStudio With R MarkdownUsing R Markdown Files Versus R-ScriptsA Little PracticeSummaryCommon ProblemsReview QuestionsPractice on Analysis and VisualizationAnnotated R FunctionsAnswersChapter 2: An Introduction to Data AnalysisLearning ObjectivesOverviewMotivating Data AnalysisThe Main Components of Data AnalysisDeveloping Hypotheses by Describing DataModel Building and EstimationDiagnosticsNext QuestionsSummaryCommon ProblemsReview QuestionsPractice on Analysis and VisualizationAnnotated R FunctionsAnswersChapter 3: Describing DataLearning ObjectivesOverviewData Sets and VariablesDifferent Kinds of VariablesDescribing Data Saves Time and EffortMeasurementSummaryCommon ProblemsReview QuestionsPractice on Analysis and VisualizationAnnotated R FunctionsAnswersChapter 4: Central Tendency and DispersionLearning ObjectivesOverviewMeasures of Central Tendency: The Mode, Mean, and MedianMean Versus MedianMeasures of Dispersion: The Range, Interquartile Range, and Standard DeviationInterquartile Range Versus Standard DeviationSummaryCommon ProblemsReview QuestionsPractice on Analysis and VisualizationAnnotated R FunctionsAnswersChapter 5: Univariate and Bivariate Descriptions of DataLearning ObjectivesOverviewThe Good, the Bad, and the OutlierFive Views of Univariate DataAre They in a Relationship?SummaryCommon ProblemsReview QuestionsPractice on Analysis and VisualizationAnnotated R FunctionsAnswersChapter 6: Transforming DataLearning ObjectivesOverviewTheoretical Reasons for Transforming DataTransforming Data for Practical ReasonsTransforming Data—Continuous to Categorical VariablesTransforming Data—Changing CategoriesBox-Cox TransformationsSummaryCommon ProblemsReview QuestionsPractice on Analysis and VisualizationAnnotated R FunctionsAnswersChapter 7: Some Principles of Displaying DataLearning ObjectivesOverviewSome Elements of StyleThe Basic Elements of a StoryDocumentation (Establishing Credibility as a Storyteller)Build an Intuition (Setting the Context)Show Causation (The Journey)From Causation to Action (The Resolution)SummaryCommon ProblemsReview QuestionsPractice on Analysis and VisualizationAnnotated R FunctionsAnswersChapter 8: The Essentials of Probability TheoryOverviewLearning ObjectivesPopulations and SamplesSample Bias and Random SamplesThe Law of Large NumbersThe Central Limit TheoremThe Standard Normal DistributionSummaryCommon ProblemsReview QuestionsPractice on Analysis and VisualizationAnnotated R FunctionsAnswersChapter 9: Confidence Intervals and Testing HypothesesLearning ObjectivesOverviewConfidence Intervals With Large SamplesSmall Samples and the t-DistributionComparing Two Sample MeansConfidence LevelsA Brief Note on Statistical Inference and CausationSummaryCommon ProblemsReview QuestionsPractice on Analysis and VisualizationAnnotated R FunctionsAnswersChapter 10: Making ComparisonsOverviewLearning ObjectivesWhy Do We Make Comparisons?Questions That Beg ComparisonsComparing Two Categorical VariablesComparing Continuous and Categorical VariablesComparing Two Continuous VariablesExploratory Data Analysis: Investigating Abortion Rates in the United StatesGood Analysis Generates Additional QuestionsSummaryCommon ProblemsReview QuestionsPractice on Analysis and VisualizationAnnotated R FunctionsAnswersChapter 11: Controlled ComparisonsLearning ObjectivesOverviewWhat Is a Controlled Comparison?Comparing Two Categorical Variables, Controlling for a ThirdComparing Two Continuous Variables, Controlling for a ThirdArguments and Controlled ComparisonsSummaryCommon ProblemsReview QuestionsPractice on Analysis and VisualizationAnnotated R FunctionsAnswersPractice on Analysis and VisualizationChapter 12: Linear RegressionLearning ObjectivesOverviewThe Advantages of Linear RegressionThe Slope and Intercept in Linear RegressionGoodness of Fit (R2 Statistic)Statistical SignificanceExamples of Bivariate RegressionsSummaryCommon ProblemsReview QuestionsPractice on Analysis and VisualizationAnnotated R FunctionsAnswersChapter 13: Multiple RegressionLearning ObjectivesOverviewWhat Is Multiple Regression?Regression Models and ArgumentsRegression Models, Theory, and EvidenceInterpreting Estimates in Multiple RegressionExample: Homicide Rate and EducationSummaryCommon ProblemsReview QuestionsPractice on Analysis and VisualizationAnnotated R FunctionsAnswersPractice on Analysis and VisualizationChapter 14: Dummies and InteractionsLearning ObjectivesOverviewWhat Is a Dummy Variable?Additive Models and Interactive ModelsBivariate Dummy Variable RegressionMultiple Regression and Dummy VariablesInteractions in Multiple RegressionSummaryCommon ProblemsReview QuestionsPractice on Analysis and VisualizationAnnotated R FunctionsAnswersChapter 15: Diagnostics I: Is Ordinary Least Squares Appropriate?Learning ObjectivesOverviewDiagnostics in Regression AnalysisProperties of Statistics and EstimatorsThe Gauss-Markov AssumptionsThe Residual PlotSummaryCommon ProblemsReview QuestionsPractice on Analysis and VisualizationAnnotated R FunctionsAnswersChapter 16: Diagnostics II: Residuals, Leverages, and Measures of InfluenceLearning ObjectivesOverviewOutliersLeveragesMeasures of InfluenceAdded Variable PlotsSummaryCommon ProblemsReview QuestionsPractice on Analysis and VisualizationAnnotated R FunctionsAnswersChapter 17: Logistic RegressionLearning ObjectivesOverviewQuestions and Problems That Require Logistic RegressionLogistic Regression Violates Gauss-Markov AssumptionsWorking With Logged OddsWorking With Predicted ProbabilitiesModel Fit With Logistic RegressionSummaryCommon ProblemsReview QuestionsPractice on Analysis and VisualizationAnnotated R FunctionsAnswersAppendix: Developing Empirical ImplicationsOverviewDeveloping Empirical ImplicationsTesting Additional Dependent VariablesTesting Additional Independent VariablesUsing Information on CasesCausal MechanismsThe Rabbit HoleGlossaryReferencesIndex