Course Introduction

Biostat 200C

Course webpages

• Github site: https://ucla-biostat-200c.github.io/2023spring/
  • slides, hw, announcements
• Burinlearn: https://bruinlearn.ucla.edu/courses/160230
  • announcements and hw submissions

What’s this course about?

• In 200B, we learn the linear models in the form \[ y = \beta_0 + \beta_1 x_1 + \cdots + \beta_p x_p + \epsilon, \] where
  1. \(y\) is a continuous response variable (or dependent variable),
     
  2. \(x_1, \ldots, x_p\) are covariates (or predictors, or independent variables), and
     
  3. \(\epsilon\) is the error term and assumed to be normally distributed and independent among observations.
• In 200C, we generalize the linear models in three directions.
  1. Generalized linear models (GLMs) handles nonnormal responses, \(y\).
     
     • binary response (disease or not)
     • proportions
     • counts
  2. Mixed effects models relaxes the independence assumption of the error term and allows correlation structure in \(\epsilon\).
     
     • Some data has a grouped, nested or hierarchical structure.
     • Repeated measures, longitudinal and multilevel data
  3. Nonparametric regression models (GAM, trees, neural networks) allow nonlinearity in the effects of predictors \(x\) on responses.

Course description

• Read syllabus and schedule for a tentative list of topics and course logistics.

• Teaching philosophy. Usually a GLM course is taught on blackboard/whiteboard with mostly math derivations. In this course, I will start from R code and then explain the theory behind it.