Statistical Modeling in R
After the end of Statistical Modeling in R, we expect you to be able to do the following:
- Fit models (linear regression, anova, mixed models, generalized linear mixed models)
- Test hypotheses
- Create model output tables and plots
Note: This workshop day is not a substitute for courses such as 801 and 802 which teach ANOVA, regression, some experimental design, etc. The goal is to teach you how to implement these models in R and extract and display the output; we will not be teaching experimental design or the statistical details of each test and model.
Timetable
| Time | Notes | Lectures and Resources |
|---|---|---|
| 9:00 - 9:15 | Introduction to Statistical Modeling | Why is statistical modeling important? Why should you do exploratory data analysis (EDA)? 1-ModelingIntro.R |
| 9:15 - 10:00 | Basic Statistical Tests | p-values, confidence intervals, t-tests, and chi-square tests, simple regression, etc. 2-BasicStatisticalTests.R |
| 10:00 - 10:15 | Break | |
| 10:15 - 12:00 | Linear Models (and more) | ANOVA, factorials, blocking, and normality assumptions 3-LinearModels.R |
| 12:00 - 1:00 | Lunch Break (on your own) | |
| 1:00 - 2:15 | Generalized Linear Models (and more) | What if my data is not normally distributed? 4-GeneralizedLinearModels.R |
| 2:15 - 2:30 | Break | |
| 2:30 - 4:00 | Workshop: Bring Your Own Data | Combine all the new R skills you learned this week to analyze your own data and walk away with results! Bonus - incorporate them into your Shiny app from yesterday! 5-ModelingWorkshop.R Note: We will have a few data sets if you need one. |
| 3:55 - 4:00 | Evaluation | Help us make the workshops better! |
Useful Links
- emmeans - See links to Vignettes
- Mixed Models by Ben Bolker