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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¶
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¶
Master’s level probability at the level of STAT 201A.
Linear algebra (Math 110 or equivalent).
R or Python coding fluency.