📊 STA 111: Probability and Statistical Inference

• Lectures
• Labs
• Homeworks
Resources

Course description

This class will provide the necessary probability and statistical background needed for students to go on to study economics, financial statistics, engineering and more advanced methods of quantitative analyses in the natural and social sciences. We will learn about the basic probability laws, random events, independence and conditional independence, expectations, and Bayes theorem. We will also cover 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, multiple linear regression and much more. A detailed course schedule will be maintained throughout the semester.

Labs will be taught using R and RStudio and will emphasize exploratory data analysis and implementation of inference procedures introduced in lecture.

Syllabus can be download here.

Prerequisites

Calculus (MATH21 or equivalent).

Textbook

• Probability and Statistics (4th Edition), by Morris H. DeGroot and Mark J. Schervish

• (Optional) OpenIntro Statistics (3rd Edition), by David M. Diez, Christopher D. Barr, and Mine Çetinkaya-Rundel

Instructor

• Shaobo Han
• Office: Old Chem 206B
• Email: shaobo.han@duke.edu
• Office hours: Tue and Thu 4:00pm-5:00pm or by appointment, Old Chem 211A.

Classes

• Lecture: Mon, Tue, Wed, Thu, and Fri 11:00 am – 12:15 pm, Social Sciences 311
• Lab: Mon and Wed, 1:30 pm – 2:45 pm, Social Sciences 124

Acknowledgement

This web page contains information, lecture notes, examples, and datasets developed by Dr. David Banks, Dr. Mine Çetinkaya-Rundel, Dr. Olanrewaju Michael Akande, Dr. Víctor Peña, and Dr. Rebecca Willett.