S631: Applied Linear Models I
Basic linear model theories. Simple linear regression and multiple linear regression: least square estimation, inference, ANOVA, Model selection and multicollinearity.
Semester(s) Offered: Fall
Class time: Monday, Wednesday : 11:15-12:30
Instructor: Arturo Valdivia - firstname.lastname@example.org
Other Contact(s): Arturo Valdivia - email@example.com
Sequence: Stat 632: Applied Linear Models II
Prerequisites: Both linear algebra and a statistics knowledge. Stat 320 and Math M301 or M303 or S303
Algebra Required?: For proofs and homework.
Calculus Required?: For derivations and homework.
Recommended follow-up classes: Most other graduate level statistics courses.
Substantive Orientation: Most units on campus including Statistics, Social Sciences, Biological Sciences, Informatics, Education.
Books used: Lecture notes; S. Weisberg. 2014. Applied Linear Regression, 4th Ed. Wiley. J. Fox. 2016. Applied Regression Analysis and Generalized Linear Models, 3rd Ed. Sage.
Applied/Theoretical: In the middle of theoretical/applied.
Formal Computing Lab?: No
Software Used: R
How the software is used: computation, programming and data analysis
Problem Sets: Yes, proofs and derivations
Data Analysis: Yes