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Statistics: Unlocking the Power of Data, 3rd 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 vi
Unit A: Data 1
Chapter 1. Collecting Data 2
1.1. The Structure of Data 4
1.2. Sampling from a Population 16
1.3. Experiments and Observational Studies 29
Chapter 2. Describing Data 46
2.1. Categorical Variables 48
2.2. One Quantitative Variable: Shape and Center 63
2.3. One Quantitative Variable: Measures of Spread 77
2.4. Boxplots and Quantitative/Categorical Relationships 93
2.5. Two Quantitative Variables: Scatterplot and Correlation 106
2.6. Two Quantitative Variables: Linear Regression 123
2.7. Data Visualization and Multiple Variables 137
Unit A: Essential Synthesis 161
Review Exercises 174
Projects Online
Unit B: Understanding Inference 193
Chapter 3. Confidence Intervals 194
3.1. Sampling Distributions 196
3.2. Understanding and Interpreting Confidence Intervals 213
3.3. Constructing Bootstrap Confidence Intervals 228
3.4. Bootstrap Confidence Intervals using Percentiles 242
Chapter 4. Hypothesis Tests 256
4.1. Introducing Hypothesis Tests 258
4.2. Measuring Evidence with P-values 272
4.3. Determining Statistical Significance 288
4.4. A Closer Look at Testing 303
4.5. Making Connections 318
Unit B: Essential Synthesis 341
Review Exercises 351
Projects Online
Unit C: Inference with Normal and t-Distributions 369
Chapter 5. Approximating with a Distribution 370
5.1. Hypothesis Tests Using Normal Distributions 372
5.2. Confidence Intervals Using Normal Distributions 387
Chapter 6. Inference for Means and Proportions 402
6.1. Inference for a Proportion
6.1-D Distribution of a Proportion 404
6.1-CI Confidence Interval for a Proportion 407
6.1-HT Hypothesis Test for a Proportion 414
6.2. Inference for a Mean
6.2-D Distribution of a Mean 419
6.2-CI Confidence Interval for a Mean 424
6.2-HT Hypothesis Test for a Mean 433
6.3. Inference for a Difference in Proportions
6.3-D Distribution of a Difference in Proportions 438
6.3-CI Confidence Interval for a Difference in Proportions 441
6.3-HT Hypothesis Test for a Difference in Proportions 446
6.4. Inference for a Difference in Means
6.4-D Distribution of a Difference in Means 452
6.4-CI Confidence Interval for a Difference in Means 455
6.4-HT Hypothesis Test for a Difference in Means 461
6.5. Paired Difference in Means 468
Unit C: Essential Synthesis 477
Review Exercises 489
Projects Online
Unit D: Inference for Multiple Parameters 505
Chapter 7. Chi-Square Tests for Categorical Variables 506
7.1. Testing Goodness-of-Fit for a Single Categorical Variable 508
7.2. Testing for an Association between Two Categorical Variables 523
Chapter 8. ANOVA to Compare Means 538
8.1. Analysis of Variance 540
8.2. Pairwise Comparisons and Inference after ANOVA 563
Chapter 9. Inference for Regression 574
9.1. Inference for Slope and Correlation 576
9.2. ANOVA for Regression 591
9.3. Confidence and Prediction Intervals 603
Chapter 10. Multiple Regression 610
10.1. Multiple Predictors 612
10.2. Checking Conditions for a Regression Model 624
10.3. Using Multiple Regression 633
Unit D: Essential Synthesis 647
Review Exercises 661
Projects Online
The Big Picture: Essential Synthesis 669
Exercises for the Big Picture: Essential Synthesis 683
Chapter P. Probability Basics 688
P.1. Probability Rules 690
P.2. Tree Diagrams and Bayes’ Rule 702
P.3. Random Variables and Probability Functions 709
P.4. Binomial Probabilities 716
P.5. Density Curves and the Normal Distribution 724
Appendix A. Chapter Summaries 737
Appendix B. Selected Dataset Descriptions 749
Partial Answers 761
Index 000
General Index 000
Data Index 000
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