Advanced Linear Models

Author

Aaron R. Caldwell, Ph.D.

đź“‘ Course Brief

Credit Hours: 3

Prerequisite: BIOS 5013: Biostatistics I or equivalent. Or my approval.

Meeting Location: Online.

  • Material lectures via VODs (Videos On Demand) with links in BlackBoard.
  • Meetings will be approximately one hour or less.
    • Links for Zoom/Teams meetings will be sent via BlackBoard announcements.

Course Location: BlackBoard https://uams.blackboard.com

Focus: the focus of this course is to learn stuff

How: hands-on learning

🎯 Learning Objectives

This course is meant to help you understand (and hopefully do!) some of most common tools in standard statistical analyses:

  • Linear regression: Can we please use “lines” to model data? Probably…
    • Simple Linear Regression
    • Multiple Linear Regression
  • Analysis of Variance (ANOVA): Applying linear models to understand the mean difference among categorical groups.
    • One-Way ANOVA
    • Block Design
    • Factorial ANOVA
  • Analysis of Covariance (ANCOVA): How categorical variables can be included in linear regression.
  • Generalized Linear Models: Other ways to view data as “linear”.
    • Logistic Regression: Turning modeling probabilities/odds into linear regression.

Maybe we’ll get to learn

  • Repeated measures: What to do when we keep track of things over time. (Briefly… its complicated.)
  • Non-parametric regression: What if we can’t put an (estimated) line to it. (Briefly)

Course Topics

  • Multiple regression and linear models for analysis of variance.
  • Experimental Designs with factorial arrangement of treatments, and multiple covariates.
  • Introduction to logistic regression.

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