Carnegie Mellon University

36617 - Applied Linear Models

Upon successful completion of this course, students should be able to properly analyze real-world atasets using linear regression and related methods in both R and SAS, use exploratory data analysis (EDA) techniques to learn salient features of the data, build appropriate models based on your EDA, diagnose any possible violations of model assumptions and, if necessary, apply remedial measures to overcome violations, perform appropriate analytical/inferential techniques to address objectives of a client/colleague, and clearly communicate the results of an analysis to a layperson.


The main topics will include:

Simple linear regression models (inference, diagnostics, and remedial measures), multiple linear egression models (inference, diagnostics, and remedial measures), analysis of variance, analysis of covariance, variable selection, and extensions of traditional linear regression models (generalized linear models, penalized regression with ridge/LASSO, semiparametric regression/smoothing).