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...

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Hauptverfasser:	Rigdon, Steven E. 1955- (VerfasserIn), Fricker, Ronald D. Jr. 1960- (VerfasserIn), Montgomery, Douglas C. 1943- (VerfasserIn)
Format:	Buch
Sprache:	English
Veröffentlicht:	Cambridge Cambridge University Press 2025
Schlagworte:	R > Programm Datenanalyse Data Science Wahrscheinlichkeitstheorie Statistik
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:	xvi, 812 Seiten Illustrationen, Diagramme
ISBN:	9781107113046 9781009568357