Statistics Using R
Cambridge University Press (Verlag)
978-1-108-71914-8 (ISBN)
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, as an online resource, R-code script files for all examples and figures included in each chapter, for students to learn from and adapt and use in their future data analytic work. Other unique features created specifically for this textbook include an online R tutorial that introduces readers to data frames and other basic elements of the R architecture, and a CRAN library of datasets and functions that is used throughout the book. Essential topics often overlooked in other introductory texts, such as data management, are covered. The textbook includes online solutions to all end-of-chapter exercises and PowerPoint slides for all chapters as additional resources, and is suitable for those who do not have a strong background in mathematics.
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.
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.
| Erscheinungsdatum | 05.08.2020 |
|---|---|
| Zusatzinfo | Worked examples or Exercises; 102 Tables, black and white; 4 Halftones, black and white; 184 Line drawings, black and white |
| Verlagsort | Cambridge |
| Sprache | englisch |
| Maße | 250 x 200 mm |
| Gewicht | 1440 g |
| Themenwelt | Geisteswissenschaften ► Psychologie |
| Mathematik / Informatik ► Mathematik ► Computerprogramme / Computeralgebra | |
| Mathematik / Informatik ► Mathematik ► Statistik | |
| Sozialwissenschaften ► Soziologie | |
| ISBN-10 | 1-108-71914-7 / 1108719147 |
| ISBN-13 | 978-1-108-71914-8 / 9781108719148 |
| Zustand | Neuware |
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