Statistics : the art and science of learning from data / Alan Agresti, Christine A Franklin and Berhard Klingenberg; Contribution by Michael Posner

Previous edition: 2014.

Includes index.

Preface PART ONE: GATHERING AND EXPLORING DATA 1. Statistics: The Art and Science of Learning from Data1.1 Using Data to Answer Statistical Questions1.2 Sample Versus Population1.3 Using Calculators and Computers Chapter Summary Chapter Problems 2. Exploring Data with Graphs and Numerical Summaries2.1 Different Types of Data2.2 Graphical Summaries of Data2.3 Measuring the Center of Quantitative Data2.4 Measuring the Variability of Quantitative Data2.5 Using Measures of Position to Describe Variability2.6 Recognizing and Avoiding Misuses of Graphical Summaries Chapter Summary Chapter Problems 3. Association: Contingency, Correlation, and Regression3.1 The Association Between Two Categorical Variables3.2 The Association Between Two Quantitative Variables3.3 Predicting the Outcome of a Variable3.4 Cautions in Analyzing Associations Chapter Summary Chapter Problems 4. Gathering Data4.1 Experimental and Observational Studies4.2 Good and Poor Ways to Sample4.3 Good and Poor Ways to Experiment4.4 Other Ways to Conduct Experimental and Nonexperimental Studies Chapter Summary Chapter Problems Part Review 1 (ONLINE) PART TWO: PROBABILITY, PROBABILITY DISTRIBUTIONS, AND SAMPLING DISTRIBUTIONS 5. Probability in Our Daily Lives5.1 How Probability Quantifies Randomness5.2 Finding Probabilities5.3 Conditional Probability5.4 Applying the Probability Rules Chapter Summary Chapter Problems 6. Probability Distributions6.1 Summarizing Possible Outcomes and Their Probabilities6.2 Probabilities for Bell-Shaped Distributions6.3 Probabilities When Each Observation Has Two Possible Outcomes Chapter Summary Chapter Problems 7. Sampling Distributions7.1 How Sample Proportions Vary Around the Population Proportion7.2 How Sample Means Vary Around the Population Mean Chapter Summary Chapter Problems Part Review 2 (ONLINE) PART THREE: INFERENTIAL STATISTICS 8. Statistical Inference: Confidence Intervals8.1 Point and Interval Estimates of Population Parameters8.2 Constructing a Confidence Interval to Estimate a Population Proportion8.3 Constructing a Confidence Interval to Estimate a Population Mean8.4 Choosing the Sample Size for a Study8.5 Using Computers to Make New Estimation Methods Possible Chapter Summary Chapter Problems 9. Statistical Inference: Significance Tests About Hypotheses9.1 Steps for Performing a Significance Test9.2 Significance Tests About Proportions9.3 Significance Tests About Means9.4 Decisions and Types of Errors in Significance Tests9.5 Limitations of Significance Tests9.6 The Likelihood of a Type II Error Chapter Summary Chapter Problems 10. Comparing Two Groups10.1 Categorical Response: Comparing Two Proportions10.2 Quantitative Response: Comparing Two Means10.3 Other Ways of Comparing Means and Comparing Proportions10.4 Analyzing Dependent Samples10.5 Adjusting for the Effects of Other Variables Chapter Summary Chapter Problems Part Review 3 (ONLINE) PART FOUR: ANALYZING ASSOCIATION AND EXTENDED STATISTICAL METHODS 11. Analyzing the Association Between Categorical Variables11.1 Independence and Dependence (Association)11.2 Testing Categorical Variables for Independence11.3 Determining the Strength of the Association11.4 Using Residuals to Reveal the Pattern of Association11.5 Fisher's Exact and Permutation Tests Chapter Summary Chapter Problems 12. Analyzing the Association Between Quantitative Variables: Regression Analysis12.1 Modeling How Two Variables Are Related12.2 Inference About Model Parameters and the Association12.3 Describing the Strength of Association12.4 How the Data Vary Around the Regression Line12.5 Exponential Regression: A Model for Nonlinearity Chapter Summary Chapter Problems 13. Multiple Regression13.1 Using Several Variables to Predict a Response13.2 Extending the Correlation and R2 for Multiple Regression13.3 Using Multiple Regression to Make Inferences13.4 Checking a Regression Model Using Residual Plots 13.5 Regression and Categorical Predictors13.6 Modeling a Categorical Response Chapter Summary Chapter Problems 14. Comparing Groups: Analysis of Variance Methods14.1 One-Way ANOVA: Comparing Several Means14.2 Estimating Differences in Groups for a Single Factor14.3 Two-Way ANOVA Chapter Summary Chapter Problems 15. Nonparametric Statistics15.1 Compare Two Groups by Ranking15.2 Nonparametric Methods for Several Groups and for Matched Pairs Chapter Summary Chapter Problems Part Review 4 (ONLINE) TablesAnswersIndexIndex of ApplicationsPhoto Credits