Statistics: Unlocking the Power of Data, 3rd Edition

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. Cover Title Page Copyright Contents Preface Unit A: Data Chapter 1: Collecting Data 1.1. The Structure of Data 1.2. Sampling from a Population 1.3. Experiments and Observational Studies Chapter 2: Describing Data 2.1. Categorical Variables 2.2. One Quantitative Variable: Shape and Center 2.3. One Quantitative Variable: Measures of Spread 2.4. Boxplots and Quantitative/Categorical Relationships 2.5. Two Quantitative Variables: Scatterplot and Correlation 2.6. Two Quantitative Variables: Linear Regression 2.7. Data Visualization and Multiple Variables Unit A: Essential Synthesis Review Exercises Projects Online Unit B: Understanding Inference Chapter 3: Confidence Intervals 3.1. Sampling Distributions 3.2. Understanding and Interpreting Confidence Intervals 3.3. Constructing Bootstrap Confidence Intervals 3.4. Bootstrap Confidence Intervals Using Percentiles Chapter 4: Hypothesis Tests 4.1. Introducing Hypothesis Tests 4.2. Measuring Evidence with P-values 4.3. Determining Statistical Significance 4.4. A Closer Look at Testing 4.5. Making Connections Unit B: Essential Synthesis Review Exercises Projects Online Unit C: Inference with Normal and t-Distributions Chapter 5: Approximating with a Distribution 5.1. Hypothesis Tests Using Normal Distributions 5.2. Confidence Intervals Using Normal Distributions Chapter 6: Inference for Means and Proportions 6.1. Inference for a Proportion 6.1-D Distribution of a Proportion 6.1-CI Confidence Interval for a Proportion 6.1-HT Hypothesis Test for a Proportion 6.2. Inference for a Mean 6.2-D Distribution of a Mean 6.2-CI Confidence Interval for a Mean 6.2-HT Hypothesis Test for a Mean 6.3. Inference for a Difference in Proportions 6.3-D Distribution of a Difference in Proportions 6.3-CI Confidence Interval for a Difference in Proportions 6.3-HT Hypothesis Test for a Difference in Proportions 6.4. Inference for a Difference in Means 6.4-D Distribution of a Difference in Means 6.4-CI Confidence Interval for a Difference in Means 6.4-HT Hypothesis Test for a Difference in Means 6.5. Paired Difference in Means Unit C: Essential Synthesis Review Exercises Projects Online Unit D: Inference for Multiple Parameters Chapter 7: Chi-Square Tests for Categorical Variables 7.1. Testing Goodness-of-Fit for a Single Categorical Variable 7.2. Testing for an Association between Two Categorical Variables Chapter 8: ANOVA to Compare Means 8.1. Analysis of Variance 8.2. Pairwise Comparisons and Inference after ANOVA Chapter 9: Inference for Regression 9.1. Inference for Slope and Correlation 9.2. ANOVA for Regression 9.3. Confidence and Prediction Intervals Chapter 10: Multiple Regression 10.1. Multiple Predictors 10.2. Checking Conditions for a Regression Model 10.3. Using Multiple Regression Unit D: Essential Synthesis Review Exercises Projects Online The Big Picture: Essential Synthesis Exercises for the Big Picture: Essential Synthesis Chapter P: Probability Basics P.1. Probability Rules P.2. Tree Diagrams and Bayes’ Rule P.3. Random Variables and Probability Functions P.4. Binomial Probabilities P.5. Density Curves and the Normal Distribution Appendix A. Chapter Summaries Appendix B. Selected Dataset Descriptions Partial Answers Index General Index Data Index EULA "This book takes a unique approach of utilizing computer simulation methods to introduce students to the key ideas of statistical inference. Methods such as bootstrap intervals and randomization tests are very intuitive to novice students and capitalize on visual learning skills students bring to the classroom. With proper use of computer support, they are accessible at very early stages of a course with little formal background. Our text introduces statistical inference through these resampling and randomization methods, not only because these methods are becoming increasingly important for statisticians in their own right but also because they are outstanding in building students' conceptual understanding of the key ideas"-- Provided by publisher