Converted source.
syllabus.md— berkeley-stat230a · spring-2025, licensed CC BY 4.0. Converted 2026-09-20 from.md. The same text in markdown, split so that every part has a URL; nothing here is rewritten.
Syllabus¶
Download full syllabus
Jump to:
(about-stat-230a)=
About Stat 230a¶
Theory of least squares estimation, interval estimation, and tests under the general linear fixed effects model with normally distributed errors. Large sample theory for non-normal linear models. Two and higher way layouts, residual analysis. Effects of departures from the underlying assumptions. Robust alternatives to least squares.
(goals)=
Goals¶
By the end of the semester you should be able to:
-
Understand and interpret ordinary least squares regression models from a mathematical perspective.
-
Adapt the basic regression model to common practical complications including violations of standard assumptions, high-dimensional regimes, and non-continuous outcomes.
-
Evaluate the quality of a regression analysis in context and suggest improvements.
-
Use R or Python to fit, report, and clearly communicate the process and results of a regression analysis.
(prerequisites)=
Prerequisites¶
-
Master’s level probability at the level of STAT 201A.
-
Linear algebra (Math 110 or equivalent).
-
R or Python coding fluency.