Course: STAT 23400
Title: Statistical Models and Method 1
Instructor(s): Andrew Goldstein
Teaching Assistant(s): Zihao Wang, Yuepeng Yang
Class Schedule: Section 2: TR 2:40 PM–4:00 PM (Remote)
Textbook(s): Diez, Barr, Çetinkaya-Rundel, OpenIntro Statistics, 4th edition. Available for free download: https://www.openintro.org/book/os/
Description: This course is recommended for students throughout the natural and social sciences who want a broad background in statistical methodology and exposure to probability models and the statistical concepts underlying the methodology. Probability is developed for the purpose of modeling outcomes of random phenomena. Random variables and their expectations are studied; including means and variances of linear combinations and an introduction to conditional expectation. Binomial, Poisson, normal and other standard probability distributions are considered. Some probability models are studied mathematically, and others are studied via computer simulation. Sampling distributions and related statistical methods are explored mathematically, studied via simulation, and illustrated on data. Methods include, but are not limited to, inference for means and proportions for one- and two-sample problems, two-way tables, correlation, and simple linear regression. Graphical and numerical data description are used for exploration, communication of results, and comparing mathematical consequences of probability models and data. Mathematics employed is to the level of single-variable differential and integral calculus and sequences and series.
Prerequisite(s): MATH 13300, 15300, or 16200
Note(s): Students may count either STAT 22000 or 23400, but not both, toward the forty-two credits required for graduation.