STA 211: Regression Analysis

STA 211 presents the mathematics underpinning linear and generalized linear models, some of the most common techniques used in applied statistics. We will see how matrix algebra enables a very practical representation of regression modeling, helps us understand how and why regression works, and offers convenient maximum likelihood estimation of regression model parameters. In STA 211, we emphasize mathematics and theory over applied data analysis, although we continuously connect the mathematical theory to topics in data analysis. This course is 0.5 credits and meets once per week.

Course info
Lecture T 3:30 - 4:45 PM Old Chemistry 116

Instructional team
Instructor   Yue Jiang M/W 9:00 - 10:00 AM Old Chemistry 207A
Teaching Assistant   Manny Mokel F 12:30 - 2:30 PM Old Chemistry 203
Teaching Assistant   Yanjiao Yang F 9:00 - 11:00 AM Old Chemistry 025
Header: The image in the header of this site is from a first edition printing of Carl Friedrich Gauss' work Theoria combinationis observationum erroribus minimis obnoxiae. In particular, it displays one of the concluding sections of the first half of the work, in which Gauss establishes the conditions under which the ordinary least squares estimator achieves the minimum variance among all possible linear unbiased estimators. This is the Gauss-Markov theorem, which, coincidentally, we will derive and study as the conclusion to the first half of our semester.