Overview

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.

Full Product Details

Author:   Robin H. Lock (St. Lawrence University) ,  Patti Frazer Lock (St. Lawrence University) ,  Kari Lock Morgan (Duke University) ,  Eric F. Lock (Duke University)
Publisher:   John Wiley & Sons Inc
Imprint:   John Wiley & Sons Inc
Edition:   3rd Revised edition
Dimensions:   Width: 21.10cm , Height: 3.60cm , Length: 27.40cm
Weight:   1.701kg
ISBN:  

9781119682165

ISBN 10:   1119682169
Pages:   864
Publication Date:   13 October 2020
Audience:   College/higher education ,  Tertiary & Higher Education
Replaced By:   9781394336623
Format:   Loose-leaf
Publisher's Status:   Active
Availability:   Available To Order  
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Table of Contents

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 Projects Online 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 Projects Online 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 Projects Online 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

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