Summary and Schedule
This is a new lesson built with The Carpentries Workbench.
| Setup Instructions | Download files required for the lesson | |
| Duration: 00h 00m | 1. Introduction to Categorical Data | What is categorical data and how is it usually represented? |
| Duration: 00h 10m | 2. Quantifying association | |
| Duration: 00h 30m | 3. Visualizing categorical data | How can I visualize categorical data in R? |
| Duration: 00h 40m | 4. Sampling schemes and probabilities |
Question 1 :::::::::::::::::::::::::::::::::::::::::::::::: |
| Duration: 00h 50m | 5. The Chi-Square test |
What are the null and alternative hypothesis of the chi-square
test? What are the expected counts under the null hypothesis? What is the test statistic for the chi-square test? How can I run the chi-square test in R? :::::::::::::::::::::::::::::::::::::::::::::::: |
| Duration: 01h 00m | 6. Categorical data and statistical power |
What determines the power of a chi-square test? Why is it difficult to determine significance for discrete data? What is the Fisher test and when should I use it? |
| Duration: 01h 10m | 7. Complications with biological data |
When are Fisher and Chisquare test not applicable for biological data? What are alternative methods? |
| Duration: 01h 20m | 8. Modeling count data |
What is a generalized linear model (GLM) and how can count data be
represented with it? How can tests for association be implemented with GLMs? How can GLMs help to account for biological variance? |
| Duration: 01h 40m | 9. Overdispersed data | Which models account for biological variability? |
| Duration: 02h 00m | Finish |
The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.
Learning goals for this lession
In this lesson, you’ll learn
- how to handle data sets containing categorical data in R,
- how to visualize categorical data,
- how to calculate effect sizes, and
- how to test for a difference in proportions.
Prerequisites
- Data handling and visualization using the
tidyversein R. We recommend- completing this tutorial (crash course, if you already have some experience in R)
- the carpentries R for ecologists (if you’re starting from scratch)
- Basics on statistical distributions (covered in this lecture)
- Basics on hypothesis testing (covered in this lesson)
Author
Dr. Sarah Kaspar (sarah.kaspar@embl.de)