Linear models in R
A workshop on linear models in R. Learning to use linear models provides a foundation for modelling, estimation, prediction, and statistical testing in R. Many commonly used statistical tests can be performed using linear models. Ideas introduced using linear models are applicable to many of the more complicated statistical and machine learning models available in R.
To be taught as a hands on workshop, typically as two half-days.
Developed by the Monash Bioinformatics Platform and taught as part of the Data Fluency program at Monash University. License is CC-BY-4. You are free to share and adapt the material so long as attribution is given.
Licence: Other (Open)
Contact: Paul Harrison paul.harrison@monash.edu
Keywords: R statistics
Additional information
Target audience: PhD student, Post-doc / Fellow, Academic
Resource type: Tutorial
Status: Active
Prerequisites:
Beginner level R.
Learning objectives:
Ability to use linear models to perform common statistical tests, estimate quantities, calculate confidence intervals, make predictions.
Ability to construct an appropriate model for data with a complicated experimental design, using R's formula syntax, and justify it by rejecting simpler models.
Gain familiarity with the R way of doing statistics by fitting and comparing models.
Date created: 2019-02-13
Date published: 2019-02-13
