Stats: Data and Models

For courses in Introductory Statistics. Encourages statistical thinking using technology, innovative methods, and a sense of humor Inspired by the 2016 GAISE Report revision, Stats: Data and Models, 5th Edition by De Veaux/Velleman/Bock uses innovative strategies to help students think critically about data — while maintaining the book’s core concepts, coverage, and most importantly, readability. By using technology and simulations to demonstrate variability at critical points throughout the course, the authors make it easier for instructors to teach and for students to understand more complicated statistical concepts later in the course (such as the Central Limit Theorem). In addition, students get more exposure to large data sets and multivariate thinking, which better prepares them to be critical consumers of statistics in the 21st century.   The 5th Edition’s approach to teaching Stats: Data and Models is revolutionary, yet it retains the book's lively tone and hallmark pedagogical features such as its Think/Show/Tell Step-by-Step Examples. Also available with MyLab Statistics MyLab™ Statistics is the teaching and learning platform that empowers instructors to reach every student. By combining trusted author content with digital tools and a flexible platform, MyLab Statistics personalizes the learning experience and improves results for each student. With MyLab Statistics and StatCrunch, an integrated web-based statistical software program, students learn the skills they need to interact with data in the real world.   Note: You are purchasing a standalone product; MyLab Statistics does not come packaged with this content. Students, if interested in purchasing this title with MyLab Statistics, ask your instructor to confirm the correct package ISBN and Course ID. Instructors, contact your Pearson representative for more information. If you would like to purchase both the physical text and MyLab Statistics, search for: 0135256216 / 9780135256213  Stats: Data and Models Plus MyLab Statistics with Pearson eText - Access Card Package Package consists of: 013516382X / 9780135163825 Stats: Data and Models 0135189691 / 9780135189696 MyLab Statistics with Pearson eText - Standalone Access Card - for Stats: Data and Models

Table of Contents:
I: EXPLORING AND UNDERSTANDING DATA 1. Stats Starts Here 1.1 What Is Statistics? 1.2 Data 1.3 Variables 1.4 Models 2. Displaying and Describing Data 2.1 Summarizing and Displaying a Categorical Variable 2.2 Displaying a Quantitative Variable 2.3 Shape 2.4 Center 2.5 Spread 3. Relationships Between Categorical Variables–Contingency Tables 3.1 Contingency Tables 3.2 Conditional Distributions 3.3 Displaying Contingency Tables 3.4 Three Categorical Variables 4. Understanding and Comparing Distributions 4.1 Displays for Comparing Groups 4.2 Outliers 4.3 Re-Expressing Data: A First Look 5. The Standard Deviation as a Ruler and the Normal Model 5.1 Using the Standard Deviation to Standardize Values 5.2 Shifting and Scaling 5.3 Normal Models 5.4 Working with Normal Percentiles 5.5 Normal Probability Plots Review of Part I: Exploring and Understanding Data II. EXPLORING RELATIONSHIPS BETWEEN VARIABLES 6. Scatterplots, Association, and Correlation 6.1 Scatterplots 6.2 Correlation 6.3 Warning: Correlation ≠ Causation 6.4 Straightening Scatterplots 7. Linear Regression 7.1 Least Squares: The Line of “Best Fit” 7.2 The Linear Model 7.3 Finding the Least Squares Line 7.4 Regression to the Mean 7.5 Examining the Residuals 7.6 R2: The Variation Accounted for by the Model 7.7 Regression Assumptions and Conditions 8. Regression Wisdom 8.1 Examining Residuals 8.2 Extrapolation: Reaching Beyond the Data 8.3 Outliers, Leverage, and Influence 8.4 Lurking Variables and Causation 8.5 Working with Summary Values 8.6 Straightening Scatterplots: The Three Goals 8.7 Finding a Good Re-Expression 9. Multiple Regression 9.1 What Is Multiple Regression? 9.2 Interpreting Multiple Regression Coefficients 9.3 The Multiple Regression Model: Assumptions and Conditions 9.4 Partial Regression Plots 9.5 Indicator Variables Review of Part II: Exploring Relationships Between Variables III. GATHERING DATA 10. Sample Surveys 10.1 The Three Big Ideas of Sampling 10.2 Populations and Parameters 10.3 Simple Random Samples 10.4 Other Sampling Designs 10.5 From the Population to the Sample: You Can't Always Get What You Want 10.6 The Valid Survey 10.7 Common Sampling Mistakes, or How to Sample Badly 11. Experiments and Observational Studies 11.1 Observational Studies 11.2 Randomized, Comparative Experiments 11.3 The Four Principles of Experimental Design 11.4 Control Groups 11.5 Blocking 11.6 Confounding Review of Part III: Gathering Data IV. RANDOMNESS AND PROBABILITY 12. From Randomness to Probability 12.1 Random Phenomena 12.2 Modeling Probability 12.3 Formal Probability 13. Probability Rules! 13.1 The General Addition Rule 13.2 Conditional Probability and the General Multiplication Rule 13.3 Independence 13.4 Picturing Probability: Tables, Venn Diagrams, and Trees 13.5 Reversing the Conditioning and Bayes' Rule 14. Random Variables 14.1 Center: The Expected Value 14.2 Spread: The Standard Deviation 14.3 Shifting and Combining Random Variables 14.4 Continuous Random Variables 15. Probability Models 15.1 Bernoulli Trials 15.2 The Geometric Model 15.3 The Binomial Model 15.4 Approximating the Binomial with a Normal Model 15.5 The Continuity Correction 15.6 The Poisson Model 15.7 Other Continuous Random Variables: The Uniform and the Exponential Review of Part IV: Randomness and Probability V. INFERENCE FOR ONE PARAMETER 16. Sampling Distribution Models and Confidence Intervals for Proportions 16.1 The Sampling Distribution Model for a Proportion 16.2 When Does the Normal Model Work? Assumptions and Conditions 16.3 A Confidence Interval for a Proportion 16.4 Interpreting Confidence Intervals: What Does 95% Confidence Really Mean? 16.5 Margin of Error: Certainty vs. Precision 16.6 Choosing the Sample Size 17. Confidence Intervals for Means 17.1 The Central Limit Theorem 17.2 A Confidence Interval for the Mean 17.3 Interpreting Confidence Intervals 17.4 Picking Our Interval up by Our Bootstraps 17.5 Thoughts About Confidence Intervals 18. Testing Hypotheses 18.1 Hypotheses 18.2 P-Values 18.3 The Reasoning of Hypothesis Testing 18.4 A Hypothesis Test for the Mean 18.5 Intervals and Tests 18.6 P-Values and Decisions: What to Tell About a Hypothesis Test 19. More About Tests and Intervals 19.1 Interpreting P-Values 19.2 Alpha Levels and Critical Values 19.3 Practical vs. Statistical Significance 19.4 Errors Review of Part V: Inference for One Parameter VI. INFERENCE FOR RELATIONSHIPS 20. Comparing Groups 20.1 A Confidence Interval for the Difference Between Two Proportions 20.2 Assumptions and Conditions for Comparing Proportions 20.3 The Two-Sample z-Test: Testing for the Difference Between Proportions 20.4 A Confidence Interval for the Difference Between Two Means 20.5 The Two-Sample t-Test: Testing for the Difference Between Two Means 20.6 Randomization Tests and Confidence Intervals for Two Means 20.7 Pooling 20.8 The Standard Deviation of a Difference 21. Paired Samples and Blocks 21.1 Paired Data 21.2 The Paired t-Test 21.3 Confidence Intervals for Matched Pairs 21.4 Blocking 22. Comparing Counts 22.1 Goodness-of-Fit Tests 22.2 Chi-Square Test of Homogeneity 22.3 Examining the Residuals 22.4 Chi-Square Test of Independence 23. Inferences for Regression 23.1 The Regression Model 23.2 Assumptions and Conditions 23.3 Regression Inference and Intuition 23.4 The Regression Table 23.5 Multiple Regression Inference 23.6 Confidence and Prediction Intervals 23.7 Logistic Regression 23.8 More About Regression Review of Part VI: Inference for Relationships VII. INFERENCE WHEN VARIABLES ARE RELATED 24. Multiple Regression Wisdom 24.1 Multiple Regression Inference 24.2 Comparing Multiple Regression Model 24.3 Indicators 24.4 Diagnosing Regression Models: Looking at the Cases 24.5 Building Multiple Regression Models 25. Analysis of Variance 25.1 Testing Whether the Means of Several Groups Are Equal 25.2 The ANOVA Table 25.3 Assumptions and Conditions 25.4 Comparing Means 25.5 ANOVA on Observational Data 26. Multifactor Analysis of Variance 26.1 A Two Factor ANOVA Model 26.2 Assumptions and Conditions 26.3 Interactions 27. Statistics and Data Science 27.1 Introduction to Data Mining Review of Part VII: Inference When Variables Are Related Parts I - V Cumulative Review Exercises Appendices Answers Credits Indexes Tables and Selected Formulas