**Course:** STAT 24400

**Title:** Statistical Theory and Method 1

**Instructor(s):** John Reinitz

**Teaching Assistant(s):** Yuhan Liu, Huanlin Zhou, Wanrong Zhu

**Class Schedule:** Sec 01: MW 3:00 PM–4:20 PM in Social Sciences 122

**Office Hours:**

**Textbook(s):** Rice, *Mathematical Statistics and Data Analysis*, 3rd edition.

**Description:** This course is the first quarter of a two-quarter systematic introduction to the principles and techniques of statistics, as well as to practical considerations in the analysis of data, with emphasis on the analysis of experimental data. This course covers tools from probability and the elements of statistical theory. Topics include the definitions of probability and random variables, binomial and other discrete probability distributions, normal and other continuous probability distributions, joint probability distributions and the transformation of random variables, principles of inference (including Bayesian inference), maximum likelihood estimation, hypothesis testing and confidence intervals, likelihood ratio tests, multinomial distributions, and chi-square tests. Examples are drawn from the social, physical, and biological sciences. The coverage of topics in probability is limited and brief, so students who have taken a course in probability find reinforcement rather than redundancy. Students who have already taken STAT 25100 have the option to take STAT 24410 (if offered) instead of STAT 24400.

**Prerequisite(s):** MATH 19520 or 20000 with a grade of B or better, or MATH 16300 or 20250 or 20300 or 20700 or STAT 24300 or PHYS 22100. Concurrent or prior linear algebra (MATH 19620 or 20250 or STAT 24300 or equivalent) is recommended for students continuing to STAT 24500.

Note(s): Some previous experience with statistics and/or probability helpful but not required. Students may count either STAT 24400 or STAT 24410, but not both, toward the forty-two credits required for graduation.