PART I: GATHERING AND EXPLORING DATA Statistics: The Art and Science of Learning from Data Using Data to Answer Statistical QuestionsSample Versus PopulationOrganizing Data, Statistical Software, and the New Field of Data ScienceChapter SummaryChapter Exercises Exploring Data with Graphs and Numerical Summaries Different Types of DataGraphical Summaries of DataMeasuring the Center of Quantitative DataMeasuring the Variability of Quantitative DataUsing Measures of Position to Describe VariabilityLinear Transformations and StandardizingRecognizing and Avoiding Misuses of Graphical SummariesChapter SummaryChapter Exercises Exploring Relationships Between Two Variables The Association Between Two Categorical VariablesThe Relationship Between Two Quantitative VariablesLinear Regression: Predicting the Outcome of a VariableCautions in Analyzing AssociationsChapter SummaryChapter Exercises Gathering Data Experimental and Observational StudiesGood and Poor Ways to SampleGood and Poor Ways to ExperimentOther Ways to Conduct Experimental and Nonexperimental StudiesChapter SummaryChapter Exercises PART II: PROBABILITY, PROBABILITY DISTRIBUTIONS, AND SAMPLINGDISTRIBUTIONS Probability in Our Daily Lives How Probability Quantifies RandomnessFinding ProbabilitiesConditional ProbabilityApplying the Probability RulesChapter SummaryChapter Exercises Random Variables and Probability Distributions Summarizing Possible Outcomes and Their ProbabilitiesProbabilities for Bell-Shaped DistributionsProbabilities When Each Observation Has Two Possible OutcomesChapter SummaryChapter Exercises Sampling Distributions How Sample Proportions Vary Around the Population ProportionHow Sample Means Vary Around the Population MeanUsing the Bootstrap to Find Sampling DistributionsChapter SummaryChapter Exercises PART III: INFERENTIAL STATISTICS Statistical Inference: Confidence Intervals Point and Interval Estimates of Population ParametersConfidence Interval for a Population ProportionConfidence Interval for a Population MeanBootstrap Confidence IntervalsChapter SummaryChapter Exercises Statistical Inference: Significance Tests About Hypotheses Steps for Performing a Significance TestSignificance Tests About ProportionsSignificance Tests About a MeanDecisions and Types of Errors in Significance TestsLimitations of Significance TestsThe Likelihood of a Type II ErrorChapter SummaryChapter Exercises Comparing Two Groups Categorical Response: Comparing Two ProportionsQuantitative Response: Comparing Two MeansComparing Two Groups with Bootstrap or Permutation ResamplingAnalyzing Dependent SamplesAdjusting for the Effects of Other VariablesChapter SummaryChapter Exercises PART IV: ANALYZING ASSOCIATION AND EXTENDED STATISTICALMETHODS Analyzing the Association Between Categorical Variables Independence and Dependence (Association)Testing Categorical Variables for IndependenceDetermining the Strength of the AssociationUsing Residuals to Reveal the Pattern of AssociationFisher's Exact and Permutation TestsChapter SummaryChapter Exercises Analyzing the Association Between Quantitative Variables: Regression Analysis Modeling How Two Variables Are RelatedInference About Model Parameters and the AssociationDescribing the Strength of AssociationHow the Data Vary Around the Regression LineExponential Regression: A Model for NonlinearityChapter SummaryChapter Exercises Multiple Regression Using Several Variables to Predict a ResponseExtending the Correlation and R2 for Multiple RegressionUsing Multiple Regression to Make InferencesChecking a Regression Model Using Residual PlotsRegression and Categorical PredictorsModeling a Categorical ResponseChapter SummaryChapter Exercises Comparing Groups: Analysis of Variance Methods One-Way ANOVA: Comparing Several MeansEstimating Differences in Groups for a Single FactorTwo-Way ANOVAChapter SummaryChapter Exercises Nonparametric Statistics Compare Two Groups by RankingNonparametric Methods for Several Groups and for Matched PairsChapter SummaryChapter Exercises AppendixAnswersIndexIndex of ApplicationsCredits

Hallmark features of this title The authors give greater attention to the analysis of proportions than many other introductory statistics texts. Concepts are introduced first with categorical data, and then with quantitative data. The importance of the statistical investigative process is emphasized in Chapter 1.Featured examples and exercises throughout use the most recent data available.The approach emphasizes using interval estimation for inference with less reliance on significance testing, and incorporates the 2016 American Statistical Association's statement on P-values.

New and updated features of this titleNew and updated content reflects the importance of the statistical investigative process in data analysis. The updated content in Chapter 1 offers an additional introduction to the opportunities and challenges with Big Data and Data Science, including a discussion of ethical considerations.A new section in Chapter 2 refers to the main features of linear transformations.There is further emphasis on the two descriptive statistics, most likely encountered by students in their daily lives (differences and ratios of proportions) in Section 3.1.An expanded discussion on multivariate thinking is presented in Section 3.3.A significantly expanded coverage of resampling methods, with a thorough discussion of the bootstrap for one and two-sample problems and the correlation coefficient, in new Sections 7.3, 8.3, and 10.3.Continued emphasis on using interval estimation for inference and less reliance on significance testing incorporates the 2016 American Statistical Association’s statement on P-values.A new section on statistical software at the end of each chapter provides commented R code, showing students how the analysis can be replicated and carried out in the statistical software R.Many new and updated featured examples and exercises use the most recent data available.