MyLab Statistics with Pearson eText -- Access Card -- for Essential Statistics (18-Weeks)

For one-semester courses in Introductory Statistics.

This is the 18-week standalone access card for MyLab Statistics. 

Data analysis for everyone

We live in a data-driven world. Students must learn to think critically with and about data, communicate their findings to others, and carefully evaluate others’ arguments. The first two-thirds of Essential Statistics cover the fundamental concepts of exploratory data analysis (data collection and summary) and inferential statistics. The remaining third returns to themes covered earlier and presents them in a new context by introducing additional statistical methods, including estimating population means and analyzing categorical variables. 

Inspired by the Guidelines for Assessment and Instruction in Statistics Education (GAISE), the authors have crafted the 3rd Edition to reflect the rise of data science — offering new features to prepare students for working with the complex data that surround us.

Personalize learning with MyLab Statistics

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.

0136570550 / 9780136570554  MYLAB STATISTICS WITH PEARSON ETEXT -- ACCESS CARD -- FOR ESSENTIAL STATISTICS (18-WEEKS), 3/e

Robert L. Gould (Ph.D., University of California, Los Angeles) is a leader in the statistics education community. He has served as chair of the American Statistical Association’s (ASA) Statistics Education Section, chair of the American Mathematical Association of Two-Year Colleges/ASA Joint Committee, and has served on the National Council of Teacher of Mathematics/ASA Joint Committee. He served on a panel of co-authors for the 2005 Guidelines for Assessment and Instruction in Statistics Education (GAISE) College Report and is co-author on the revision for the GAISE K-12 Report. As lead principal investigator of the NSF-funded Mobilize Project, he led the development of the first high school level data science course, which is taught in the Los Angeles Unified School District and several other districts. 

Rob teaches in the Department of Statistics at UCLA, where he directs the undergraduate statistics program and is director of the UCLA Center for Teaching Statistics. In recognition for his activities in statistics education, in 2012 Rob was elected Fellow of the American Statistical Association. He is the 2019 recipient of the ASA Waller Distinguished Teaching Award and the USCOTS Lifetime Achievement Award. In his free time, Rob plays the cello and enjoys attending concerts of all types and styles.

Rebecca K. Wong has taught mathematics and statistics at West Valley College for more than twenty years. She enjoys designing activities to help students explore statistical concepts and encouraging students to apply those concepts to areas of personal interest.

Rebecca earned at B.A. in mathematics and psychology from the University of California, Santa Barbara, an M.S.T. in mathematics from Santa Clara University, and an Ed.D. in Educational Leadership from San Francisco State University. She has been recognized for outstanding teaching by the National Institute of Staff and Organizational Development and the California Mathematics Council of Community Colleges. When not teaching, Rebecca is an avid reader and enjoys hiking trails with friends.

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 Moorpark Community College. 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, Colleen sings, has been an avid skier, and enjoys time with her family.

Preface

Index of Applications

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

         Data Project–How Are Data Stored?

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

         Data Project–Asking Questions

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

         Data Project–The Statistical Investigation Cycle

4. Regression Analysis: Exploring Associations between Variables

         Case Study—Forecasting Home Prices

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

         Data Project–Data Moves

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

         Data Project–Submitting Data

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)

         Data Project–Generating Random Numbers

7. Survey Sampling and Inference

         Case Study—Spring Break Fever: Just What the Doctors Ordered?

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

         Data Project–Population Proportions

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

         Data Project–Dates as Data

9. Inferring Population Means

         Case Study—You Look Sick! Are You Sick?

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

         Data Project–Data Structures

10. Analyzing Categorical Variables and Interpreting Research

         Case Study—Popping Better Popcorn

10.1 The Basic Ingredients for Testing with Categorical Variables

10.2 Chi-Square Tests for Associations between Categorical Variables

10.3 Reading Research Papers

         Data Project–Think Small

Appendix A Tables

Appendix B Check Your Tech Answers

Appendix C Credits

Index

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