Indiana University Bloomington

Statistics S631
Applied Linear Models I

Contact: Arturo Valdivia (artvaldi@indiana.edu)
Offered: Fall, 2016
Class Time: 11:15-12:30 BH 347
Class Days: M, W
Capacity:
Sequence: Stat 632: Applied Linear Models II
Pre-Requisites: 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.
Instructor: Arturo Valdivia
Days Per Week Offered:
Website:
Recommended follow-up classes: Most other graduate level statistics courses.
Syllabus: No Syllabus Avaliable
Keywords: linear regression based on linear models theory
Description: Basic linear model theories. Simple linear regression and multiple linear regression: least square estimation, inference, ANOVA, Model selection and multicollinearity.
Books: Lecture notes; S. Weisberg. 2014. Applied Linear Regression, 4th Ed. Wiley. J. Fox. 2016. Applied Regression Analysis and Generalized Linear Models, 3rd Ed. Sage.
Substantive Orientation: Most units on campus including Statistics, Social Sciences, Biological Sciences, Informatics, Education.
Applied/Theoretical: In the middle of theoretical ? applied.
Formal Computing Lab: No
Software: R
How Software is Used: Computation, programming and data analysis.
Problem Sets: Yes, proofs and derivations.
Data Analysis: Yes.
Presentation: No.
Exams: Yes
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