# DSCI 562: Regression II

Approaches when faced with special conditions in regression, and consequences of ignoring these conditions.

## Course Learning Objectives

By the end of the course, students are expected to:

• Describe the risk and value of making parametric assumptions in regression.
• Fit model functions that represent probabilistic quantities besides the mean.
• Identify situations where standard linear regression is sub-optimal, and apply alternative regression methods that better address the situation.

Check out the About page for a description of the course.

## Assessments

Deliverable Weight
lab assignment 1 15%
lab assignment 2 15%
quiz 1 20%
lab assignment 3 15%
lab assignment 4 15%
quiz 2 20%

## Lecture Schedule

Note: Topics covered are conditional on time available.

Lecture Date Topic
1 2019-02-04 Model functions in regression
2 2019-02-06 Regression on restricted scales: GLM and transformations
3 2019-02-11 Regression beyond the mean Part I: variance, quantiles
4 2019-02-13 Regression beyond the mean Part II: probabilistic forecasts, robust regression
5 2019-02-25 Regression on censored response data: survival analysis
6 2019-02-27 Regression on ordinal response data: proportional odds model
7 2019-03-04 Regression in many groups: mixed effects models
8 2019-03-06 Regression when data are missing

If time remains, here are some topics we could cover:

• Regression in between linear and non-parametric: Generalized Additive Models
• Copula Regression
• Non-identifiability
• Heavy-tailed distributions

## Reference Material

• Julian J. Faraway. Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models, Second Edition (Chapman & Hall/CRC Texts in Statistical Science), 2016.
• For survival analysis: David G. Kleinbaum, Mitchel Klein (2012) Survival analysis: a self-learning text, 3rd edition
• Non-technical explanation of survival analysis, with a nice succinct summary along the side of each page.
• Recommends epidemiological background, but we will avoid those parts.