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session1
0.1.0
Lecture and Lab
Lecture notes
Lecture notes PDF
Lab materials
Lecture
Learning objectives
identify systematic and random components of a multiple linear regression model
define terminology used in a multiple linear regression model
define and explain the use of dummy variables
interpret multiple linear regression coefficients for continuous and categorical variables
use model formulae to multiple linear models
define and interpret interactions between variables
interpret ANOVA tables
Outline
multiple regression terminology and notation
continuous & categorical predictors
interactions
ANOVA tables
Model formulae
Lab
Learning objectives
Load a tab-separated dataset into R
Create a simple scatterplot using
ggplot2
Fit and analyze a multiple linear regression model
Compare two nested models using a nested Analysis of Variance (partial F test)
Exercises
Load the
cholesterol.tsv
dataset into R
Fit linear models with age, state, and interaction terms as predictors
Compare a simple linear regression to a multiple linear regression using Analysis of Variance partial F-test
Use backwards selection from a full model with interactions to choose the best prediction model
Links
Browse source code
Full course notes
git clone
License
Full license
CC BY 4.0
Citation
Citing session1
Developers
Levi Waldron
Author, maintainer
Dev status