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Using numerous examples with real data, this textbook closely integrates the learning of statistics with the learning of R.
List of contents
Preface; Acknowledgments; 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; Appendix A. Data Set Descriptions; Appendix B. .R Files and Datasets in R Format; Appendix C. Statistical Tables; Appendix D. References; Appendix E. Solutions to End of Chapter Exercises; Index.
About the author
Sharon Lawner Weinberg is Professor of Applied Statistics and Psychology, and the former Vice Provost for Faculty Affairs, at New York University (NYU). She is the recipient of the NYU Steinhardt Outstanding Teaching Award, and has taught statistics at both undergraduate and graduate levels. Her research has been supported by federal agencies and private foundations.Daphna Harel is Associate Professor of Applied Statistics at New York University. She is known for her innovative approach to teaching both introductory and advanced statistics. Her research has been supported by federal agencies and foundations, such as the National Institutes for Health and the Canadian Institutes for Health Research.Sarah Knapp Abramowitz is Professor of Mathematics and Computer Science, Department Chair, and Co-ordinator of Statistics Instruction at Drew University. She is Associate Editor of the Journal of Statistics Education and has presented at national conferences on topics related to the teaching of statistics.
Summary
Using numerous examples with real data, this textbook closely integrates the learning of statistics with the learning of R. It is suitable for introductory-level learners, allows for curriculum flexibility, and includes R-code script files for students to learn from and adapt and use in their future data analytic work.
