Douglas C. Montgomery, Jr Fricker, Steven E. Rigdon

Introduction to Probability and Statistics for Data Science

with R

• Gebundenes Buch

Produktbeschreibung

This textbook is designed for students in statistics, data science, biostatistics, engineering, and physical science programs who need a solid course in the fundamental concepts, methods and theory of statistics to understand, use, and build on modern statistical techniques for complex problems. Examples and exercises incorporate data and R code.

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Produktdetails

• Verlag: Cambridge University Press
• Seitenzahl: 828
• Erscheinungstermin: 30. September 2024
• Englisch
• ISBN-13: 9781107113046
• ISBN-10: 1107113040
• Artikelnr.: 70288897

Autorenporträt

Steven E. Rigdon is Professor of Biostatistics at Saint Louis University. He is a fellow of the American Statistical Association and is the author of Statistical Methods for the Reliability of Repairable Systems Calculus, 8th and 9th editions, Monitoring the Health of Populations by Tracking Disease Outbreaks (2020), and Design of Experiments for Reliability Achievement (2022). He has received the Waldo Vizeau Award for technical contributions to quality, the Soren Bisgaard Award, and the Paul Simon Award for linking teaching and research. He is also Distinguished Research Professor Emeritus at Southern Illinois University Edwardsville.

Inhaltsangabe

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.