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9780133956504 / 0133956504Introductory Statistics Plus NEW MyStatLab with Pearson eText -- Access Card Package, 2/e
We live in a data-driven world, and the goal of this text is to teach students how to access and analyze these data critically. Authors Rob Gould and Colleen Ryan emphasize that learning statistics extends beyond the classroom to an essential life skill, and want students to develop a "data habit of mind." Regardless of their math backgrounds, students will learn how to think about data and how to reason using data. With a clear, unintimidating writing style and carefully chosen pedagogy, this text makes data analysis accessible to all students.
Robert L. Gould (Ph.D., University of California-San Diego) is a leader in the statistics education community. He has served as chair of the American Statistical Association's Committee on Teacher Enhancement, has served as chair of the ASA's Statistics Education Section, and was a co-author of the Guidelines for Assessment in Instruction on Statistics Education (GAISE) College Report. As the associate director of professional development for CAUSE (Consortium for the Advancement of Undergraduate Statistics Education), he has worked closely with the American Mathematical Association of Two-Year Colleges (AMATYC) to provide traveling workshops and summer institutes in statistics (he also presented an AMATYC summer institute in 2009). For over ten years, he has served as Vice-Chair of Undergraduate Studies at the UCLA Department of Statistics, and he is Director of the UCLA Center for the Teaching of Statistics. In 2012, Rob was elected Fellow of the American Statistical Association. Colleen N. Ryan has taught statistics, chemistry, and physics to diverse community college students for decades. She taught at Oxnard College from 1975 to 2006, where she earned the Teacher of the Year Award. Colleen currently teaches statistics part-time at California Lutheran University. She often designs her own lab activities. Her passion is to discover new ways to make statistical theory practical, easy to understand, and sometimes even fun. Colleen earned a B.A. in physics from Wellesley College, an M.A.T. in physics from Harvard University, and an M.A. in chemistry from Wellesley College. Her first exposure to statistics was with Frederick Mosteller at Harvard. In her spare time, she sings with the Oaks Chamber Singers, has been an avid skier in the past, and enjoys time with her family.
1. Introduction to Data Case Study: Deadly Cell Phones? 1.1 What Are Data? 1.2 Classifying and Storing Data 1.3 Organizing Categorical Data 1.4 Collecting Data to Understand Causality Exploring Statistics: Collecting a Table of Different Kinds of Data 2. Picturing Variation with Graphs Case Study: Student-to-Teacher Ratio at Colleges 2.1 Visualizing Variation in Numerical Data 2.2 Summarizing Important Features of a Numerical Distribution 2.3 Visualizing Variation in Categorical Variables 2.4 Summarizing Categorical Distributions 2.5 Interpreting Graphs Exploring Statistics: Personal Distance 3. Numerical Summaries of Center and Variation Case Study: Living in a Risky World 3.1 Summaries for Symmetric Distributions 3.2 What's Unusual? The Empirical Rule and z-Scores 3.3 Summaries for Skewed Distributions 3.4 Comparing Measures of Center 3.5 Using Boxplots for Displaying Summaries Exploring Statistics: Does Reaction Distance Depend on Gender? 4. Regression Analysis: Exploring Associations between Variables Case Study: Catching Meter Thieves 4.1 Visualizing Variability with a Scatterplot 4.2 Measuring Strength of Association with Correlation 4.3 Modeling Linear Trends 4.4 Evaluating the Linear Model Exploring Statistics: Guessing the Age of Famous People 5. Modeling Variation with Probability Case Study: SIDS or Murder? 5.1 What Is Randomness? 5.2 Finding Theoretical Probabilities 5.3 Associations in Categorical Variables 5.4 Finding Empirical Probabilities Exploring Statistics: Let's Make a Deal: Stay or Switch? 6. Modeling Random Events: The Normal and Binomial Models Case Study: You Sometimes Get More Than You Pay For 6.1 Probability Distributions Are Models of Random Experiments 6.2 The Normal Model 6.3 The Binomial Model (Optional) Exploring Statistics: ESP with Coin Flipping 7. Survey Sampling and Inference Case Study: Spring Break Fever: Just What the Doctors Ordered? 30 7.1 Learning about the World through Surveys 7.2 Measuring the Quality of a Survey 7.3 The Central Limit Theorem for Sample Proportions 7.4 Estimating the Population Proportion with Confidence Intervals 7.5 Comparing Two Population Proportions with Confidence Exploring Statistics: Simple Random Sampling Prevents Bias 8. Hypothesis Testing for Population Proportions Case Study: Dodging the Question 8.1 The Essential Ingredients of Hypothesis Testing 8.2 Hypothesis Testing in Four Steps 8.3 Hypothesis Tests in Detail 8.4 Comparing Proportions from Two Populations Exploring Statistics: Identifying Flavors of Gum through Smell 9. Inferring Population Means Case Study: Epilepsy Drugs and Children 9.1 Sample Means of Random Samples 9.2 The Central Limit Theorem for Sample Means 9.3 Answering Questions about the Mean of a Population 9.4 Hypothesis Testing for Means 9.5 Comparing Two Population Means 9.6 Overview of Analyzing Means Exploring Statistics: Pulse Rates 10. Associations between Categorical Variables Case Study: Popping Better Popcorn 10.1 The Basic Ingredients for Testing with Categorical Variables 10.2 The Chi-Square Test for Goodness of Fit 10.3 Chi-Square Tests for Associations between Categorical Variables 10.4 Hypothesis Tests When Sample Sizes Are Small Exploring Statistics: Skittles 11. Multiple Comparisons and Analysis of Variance Case Study: Seeing Red 11.1 Multiple Comparisons 11.2 The Analysis of Variance 11.3 The ANOVA Test 11.4 Post-Hoc Procedures Exploring Statistics: Recovery Heart Rate 12. Experimental Design: Controlling Variation Case Study: Does Stretching Improve Athletic Performance? 12.1 Variation Out of Control 12.2 Controlling Variation in Surveys 12.3 Reading Research Papers Exploring Statistics: Reporting on Research Abstracts 13. Inference without Normality Case Study: Contagious Yawns 13.1 Transforming Data 13.2 The Sign Test for Paired Data 13.3 Mann-Whitney Test for Two Independent Groups 13.4 Randomization Tests Exploring Statistics: Balancing on One Foot 14. Inference for Regression Case Study: Building a Better Oyster Shucker 14.1 The Linear Regression Model 14.2 Using the Linear Model 14.3 Predicting Values and Estimating Means Exploring Statistics: Older and Slower? Appendix A Tables Appendix B Check Your Tech Answers Appendix C Answers to Odd-Numbered Exercises Appendix D Credits Index