Course Description
This course provides the probability and statistical background needed for further study in economics, financial statistics, engineering, and quantitative methods in the natural and social sciences.
Topics include basic probability laws, random events, independence and conditional independence, expectations, Bayes’ theorem, discrete and continuous random variables, density and distribution functions, point estimation, maximum likelihood estimation, confidence intervals, bootstrap methods, Bayesian inference, hypothesis testing, simple linear regression, and multiple linear regression.
Labs use R and RStudio and emphasize exploratory data analysis and the implementation of inference procedures introduced in lecture.
The course syllabus is available here.
Prerequisites
Calculus (MATH 21 or equivalent).
Textbooks
- Probability and Statistics (4th Edition), Morris H. DeGroot and Mark J. Schervish.
- OpenIntro Statistics (3rd Edition), David M. Diez, Christopher D. Barr, and Mine Çetinkaya-Rundel. (Optional)
Instructor
Shaobo Han
Duke University
Classes
- Lecture: Monday–Friday, 11:00 AM–12:15 PM, Social Sciences 311
- Lab: Monday and Wednesday, 1:30–2:45 PM, Social Sciences 124
Course Materials
Acknowledgment
This course website contains information, lecture notes, examples, and datasets developed in part by David Banks, Mine Çetinkaya-Rundel, Olanrewaju Michael Akande, Víctor Peña, and Rebecca Willett.