Statistics (3^rd Ed.)
Unlocking the Power of Data

Statistics: Unlocking the Power of Data, 3^rd Edition is designed for an introductory statistics course focusing on data analysis with real-world applications.  Students use simulation methods to effectively collect, analyze, and interpret data to draw conclusions.  Randomization and bootstrap interval methods introduce the fundamentals of statistical inference, bringing concepts to life through authentically relevant examples. More traditional methods like t-tests, chi-square tests, etc. are introduced after students have developed a strong intuitive understanding of inference through randomization methods. While any popular statistical software package may be used, the authors have created StatKey to perform simulations using data sets and examples from the text.  A variety of videos, activities, and a modular chapter on probability are adaptable to many classroom formats and approaches.

Preface xi

Unit A: Data 1

Chapter 1. Collecting Data 2

1.1. The Structure of Data 4

1.2. Sampling from a Population 17

1.3. Experiments and Observational Studies 31

Chapter 2. Describing Data 52

2.1. Categorical Variables 54

2.2. One Quantitative Variable: Shape and Center 72

2.3. One Quantitative Variable: Measures of Spread 86

2.4. Boxplots and Quantitative/Categorical Relationships 103

2.5. Two Quantitative Variables: Scatterplot and Correlation 117

2.6. Two Quantitative Variables: Linear Regression 136

2.7. Data Visualization and Multiple Variables 152

Unit A: Essential Synthesis 177

Review Exercises 190

Projects Online

Unit B: Understanding Inference 211

Chapter 3. Confidence Intervals 212

3.1. Sampling Distributions 214

3.2. Understanding and Interpreting Confidence Intervals 232

3.3. Constructing Bootstrap Confidence Intervals 248

3.4. Bootstrap Confidence Intervals Using Percentiles 263

Chapter 4. Hypothesis Tests 278

4.1. Introducing Hypothesis Tests 280

4.2. Measuring Evidence with P-values 295

4.3. Determining Statistical Significance 316

4.4. A Closer Look at Testing 333

4.5. Making Connections 349

Unit B: Essential Synthesis 371

Review Exercises 381

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Unit C: Inference with Normal and t-Distributions 399

Chapter 5. Approximating with a Distribution 400

5.1. Hypothesis Tests Using Normal Distributions 402

5.2. Confidence Intervals Using Normal Distributions 417

Chapter 6. Inference for Means and Proportions 430

6.1. Inference for a Proportion

6.1-D Distribution of a Proportion 432

6.1-CI Confidence Interval for a Proportion 435

6.1-HT Hypothesis Test for a Proportion 442

6.2. Inference for a Mean

6.2-D Distribution of a Mean 448

6.2-CI Confidence Interval for a Mean 454

6.2-HT Hypothesis Test for a Mean 463

6.3. Inference for a Difference in Proportions

6.3-D Distribution of a Difference in Proportions 469

6.3-CI Confidence Interval for a Difference in Proportions 472

6.3-HT Hypothesis Test for a Difference in Proportions 477

6.4. Inference for a Difference in Means

6.4-D Distribution of a Difference in Means 485

6.4-CI Confidence Interval for a Difference in Means 488

6.4-HT Hypothesis Test for a Difference in Means 494

6.5. Paired Difference in Means 502

Unit C: Essential Synthesis 513

Review Exercises 525

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Unit D: Inference for Multiple Parameters 543

Chapter 7. Chi-Square Tests for Categorical Variables 544

7.1. Testing Goodness-of-Fit for a Single Categorical Variable 546

7.2. Testing for an Association between Two Categorical Variables 562

Chapter 8. ANOVA to Compare Means 578

8.1. Analysis of Variance 580

8.2. Pairwise Comparisons and Inference after ANOVA 604

Chapter 9. Inference for Regression 614

9.1. Inference for Slope and Correlation 616

9.2. ANOVA for Regression 632

9.3. Confidence and Prediction Intervals 645

Chapter 10. Multiple Regression 652

10.1. Multiple Predictors 654

10.2. Checking Conditions for a Regression Model 670

10.3. Using Multiple Regression 679

Unit D: Essential Synthesis 693

Review Exercises 707

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The Big Picture: Essential Synthesis 715

Exercises for the Big Picture: Essential Synthesis 729

Chapter P. Probability Basics 734

P.1. Probability Rules 736

P.2. Tree Diagrams and Bayes’ Rule 748

P.3. Random Variables and Probability Functions 755

P.4. Binomial Probabilities 762

P.5. Density Curves and the Normal Distribution 770

Appendix A. Chapter Summaries 783

Appendix B. Selected Dataset Descriptions 795

Partial Answers 809

Index

General Index 835

Data Index 838