## S631: Applied Linear Models I

### Class Description

Basic linear model theories. Simple linear regression and multiple linear regression: least square estimation, inference, ANOVA, Model selection and multicollinearity.

### Class Information

**Semester(s): **Fall

**Semester(s) Offered: ** Fall

**Class time: **
Monday, Wednesday : 11:15-12:30

### Contact Information

**Instructor: **Arturo Valdivia - artvaldi@indiana.edu

**Other Contact(s): ** Arturo Valdivia - artvaldi@indiana.edu

### Other Details

**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

**Presentations: ** No

**Exams: ** Yes