Introduction to Probability and Statistics for Data Science: with R
Introduction to Probability and Statistics for Data Science provides a solid course in the fundamental concepts, methods and theory of statistics for students in statistics, data science, biostatistics, engineering, and physical science programs. It teaches students to understand, use, and build on...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New York
Cambridge University Press
2024
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| Zusammenfassung: | Introduction to Probability and Statistics for Data Science provides a solid course in the fundamental concepts, methods and theory of statistics for students in statistics, data science, biostatistics, engineering, and physical science programs. It teaches students to understand, use, and build on modern statistical techniques for complex problems. The authors develop the methods from both an intuitive and mathematical angle, illustrating with simple examples how and why the methods work. More complicated examples, many of which incorporate data and code in R, show how the method is used in practice. Through this guidance, students get the big picture about how statistics works and can be applied. This text covers more modern topics such as regression trees, large scale hypothesis testing, bootstrapping, MCMC, time series, and fewer theoretical topics like the Cramer-Rao lower bound and the Rao-Blackwell theorem. It features more than 250 high-quality figures, 180 of which involve actual data. Data and R are code available on our website so that students can reproduce the examples and do hands-on exercises |
| Beschreibung: | Part I. Descriptive Statistics & Data Science: 1. Introduction; 2. Descriptive statistics; 3. Data visualization; Part II. Probability: 4. Basic probability; 5. Random variables; 6. Discrete distributions; 7. Continuous distribution; Part III. Classical Statistical Inference: 8. About data & data collection; 9. Sampling distributions; 10. Point estimation; 11. Confidence intervals; 12. Hypothesis testing; 13. Hypothesis tests for two or more samples; 14. Hypothesis tests for discrete data; 15. Regression; Part IV. Bayesian and Other Computer Intensive Methods: 16. Bayesian methods; 17. Time series methods; 18. The jackknife and bootstrap; Part V. Advanced Topics in Inference & Data Science: 19. Generalized linear models and regression trees; 20. Cross-validation and estimates of prediction error; 21. Large-scale hypothesis testing and the false discovery rate; Appendix. More About R. |
| Beschreibung: | 828 Seiten |
| ISBN: | 9781009568357 |
Internformat
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| 500 | |a Part I. Descriptive Statistics & Data Science: 1. Introduction; 2. Descriptive statistics; 3. Data visualization; Part II. Probability: 4. Basic probability; 5. Random variables; 6. Discrete distributions; 7. Continuous distribution; Part III. Classical Statistical Inference: 8. About data & data collection; 9. Sampling distributions; 10. Point estimation; 11. Confidence intervals; 12. Hypothesis testing; 13. Hypothesis tests for two or more samples; 14. Hypothesis tests for discrete data; 15. Regression; Part IV. Bayesian and Other Computer Intensive Methods: 16. Bayesian methods; 17. Time series methods; 18. The jackknife and bootstrap; Part V. Advanced Topics in Inference & Data Science: 19. Generalized linear models and regression trees; 20. Cross-validation and estimates of prediction error; 21. Large-scale hypothesis testing and the false discovery rate; Appendix. More About R. | ||
| 520 | |a Introduction to Probability and Statistics for Data Science provides a solid course in the fundamental concepts, methods and theory of statistics for students in statistics, data science, biostatistics, engineering, and physical science programs. It teaches students to understand, use, and build on modern statistical techniques for complex problems. The authors develop the methods from both an intuitive and mathematical angle, illustrating with simple examples how and why the methods work. More complicated examples, many of which incorporate data and code in R, show how the method is used in practice. Through this guidance, students get the big picture about how statistics works and can be applied. This text covers more modern topics such as regression trees, large scale hypothesis testing, bootstrapping, MCMC, time series, and fewer theoretical topics like the Cramer-Rao lower bound and the Rao-Blackwell theorem. It features more than 250 high-quality figures, 180 of which involve actual data. Data and R are code available on our website so that students can reproduce the examples and do hands-on exercises | ||
| 700 | 1 | |a Fricker, Jr, Ronald D. |e Sonstige |4 oth | |
| 700 | 1 | |a Montgomery, Douglas C. |e Sonstige |4 oth | |
| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-035395921 | |
Datensatz im Suchindex
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| spelling | Rigdon, Steven E. Verfasser aut Introduction to Probability and Statistics for Data Science with R New York Cambridge University Press 2024 828 Seiten txt rdacontent n rdamedia nc rdacarrier Part I. Descriptive Statistics & Data Science: 1. Introduction; 2. Descriptive statistics; 3. Data visualization; Part II. Probability: 4. Basic probability; 5. Random variables; 6. Discrete distributions; 7. Continuous distribution; Part III. Classical Statistical Inference: 8. About data & data collection; 9. Sampling distributions; 10. Point estimation; 11. Confidence intervals; 12. Hypothesis testing; 13. Hypothesis tests for two or more samples; 14. Hypothesis tests for discrete data; 15. Regression; Part IV. Bayesian and Other Computer Intensive Methods: 16. Bayesian methods; 17. Time series methods; 18. The jackknife and bootstrap; Part V. Advanced Topics in Inference & Data Science: 19. Generalized linear models and regression trees; 20. Cross-validation and estimates of prediction error; 21. Large-scale hypothesis testing and the false discovery rate; Appendix. More About R. Introduction to Probability and Statistics for Data Science provides a solid course in the fundamental concepts, methods and theory of statistics for students in statistics, data science, biostatistics, engineering, and physical science programs. It teaches students to understand, use, and build on modern statistical techniques for complex problems. The authors develop the methods from both an intuitive and mathematical angle, illustrating with simple examples how and why the methods work. More complicated examples, many of which incorporate data and code in R, show how the method is used in practice. Through this guidance, students get the big picture about how statistics works and can be applied. This text covers more modern topics such as regression trees, large scale hypothesis testing, bootstrapping, MCMC, time series, and fewer theoretical topics like the Cramer-Rao lower bound and the Rao-Blackwell theorem. It features more than 250 high-quality figures, 180 of which involve actual data. Data and R are code available on our website so that students can reproduce the examples and do hands-on exercises Fricker, Jr, Ronald D. Sonstige oth Montgomery, Douglas C. Sonstige oth |
| spellingShingle | Rigdon, Steven E. Introduction to Probability and Statistics for Data Science with R |
| title | Introduction to Probability and Statistics for Data Science with R |
| title_auth | Introduction to Probability and Statistics for Data Science with R |
| title_exact_search | Introduction to Probability and Statistics for Data Science with R |
| title_full | Introduction to Probability and Statistics for Data Science with R |
| title_fullStr | Introduction to Probability and Statistics for Data Science with R |
| title_full_unstemmed | Introduction to Probability and Statistics for Data Science with R |
| title_short | Introduction to Probability and Statistics for Data Science |
| title_sort | introduction to probability and statistics for data science with r |
| title_sub | with R |
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