Statistics Using R

Sharon L. Weinberg is Professor of Applied Statistics and Psychology at New York University and formerly Vice Provost for Faculty Affairs at New York University, USA. She has taught, over many years in higher education, a broad range of statistics courses at both the undergraduate and graduate level, from introductory to advanced. She is a recipient of the NYU Steinhardt Teaching Excellence Award, the NYU Steinhardt Daniel Griffiths Award for Distinguished Research, and a four-time recipient of the Outstanding Reviewer Award for her work on the Editorial Board of the Educational Researcher, American Educational Research Association's (AERA's) flagship journal, as a reviewer of manuscripts submitted for publication. Her research has been supported by grants from both federal and private agencies, including the IES, NSF, and the Sloan Foundation, and she has published numerous papers on her research. She is the co-editor with NYU colleague, Lisa Stulberg, of Diversity in American Higher Education: Toward a More Comprehensive Approach (Routledge, 2011). She has also published numerous expository papers on methodology as book chapters and journal articles. Daphna Harel is Associate Professor of Applied Statistics at New York University, USA. She is known for her innovative pedagogical approach to the teaching of statistics, from the introductory undergraduate to the advanced graduate level. She earned her BSc and PhD from the McGill University Department of Mathematics and Statistics, Canada. Her research has been supported by federal agencies and foundations, such as the National Institutes for Health, the Canadian Institutes for Health Research, and the Spencer Foundation. As a highly productive researcher, she has published numerous peer-reviewed articles across statistics, as well as several domain areas. Sarah Knapp Abramowitz is the John H. Evans Professor and Chair of the Department of Mathematics and Computer Science at Drew University, USA. She earned an A.B. in Mathematics from Cornell University and a Ph.D. in Mathematics Education from New York University, USA. She is an Associate Editor of the Journal of Statistics and Data Science Education and has published expository papers and presented at national conferences on topics related to the teaching of statistics. She is currently teaching an undergraduate course in statistics that uses this text along with a flipped approach, in which students watch instructor-created videos outside of class and spend class time participating in student-centered, activity-based learning.

• 1. Introduction; 2. Examining Univariate Distributions; 3. Measures of Location, Spread, and Skewness; 4. Re-Expressing Variables; 5. Exploring Relationships between Two Variables; 6. Simple Linear Regression; 7. Probability Fundamentals; 8. Theoretical Probability Models; 9. The Role of Sampling in Inferential Statistics; 10. Inferences Involving the Mean of a Single Population when Σ is Known; 11. Inferences Involving the Mean When Σ is Not Known: One- and Two-Sample Designs; 12. Research Design: Introduction and Overview; 13. One-Way Analysis of Variance; 14. Two-Way Analysis of Variance; 15. Correlation and Simple Regression as Inferential Techniques; 16. An Introduction to Multiple Regression; 17. Two-Way Interactions in Multiple Regression; 18. Nonparametric Methods; 19. Accessing Data from Public Use Sources.

'Statistics Using R is an engaging and accessible introduction to the practice of statistics using the powerful R environment. Readers will benefit from the book's clear, step-by-step demonstrations of statistical techniques and superb sample exercises, which use data and applications often encountered in the real world. Before they know it, users of this text will be confidently using R to analyze and interpret their own data.' Sean P. Corcoran, Vanderbilt University, USA