Analyzing Continuous Data

In this unit, you will become familiar with running and plotting correlations and regression in R.

Follow the links to each lecture, lab, and reading.

Scroll down to download the SWIRL lessons.

Lesson 1. How to test for correlation and causation

Lecture: Testing associations

Learning Goals:

• Run correlation and simple regression models,
• Interpret output,
• Extract and plot details from output.

SWIRL: Testing_Associations

Lab: Unit 4: Lab 1

Reading: Crawley, M. The R Book. Ch 10 Regression.

Functions: ks.test(), cor(), lm(), abline(), coef().

Lesson 2. How to work with multiple predictors

Lecture: Multiple regression

Learning Goals:

• Understand how to code different statistical relationships,
• Interpret model output and diagnose model fit.

SWIRL: Testing_Associations

Lab: Unit 4: Lab 2

Reading: Crawley, M. The R Book. Ch 12. Analysis of Covariance.

Functions: lm()

Lesson 3. How to deal with binomial and count data

Lecture:

• Binomial data

• Count data

Learning Goals:

• Understand binomial and count data,
• Know how to analyse and plot these data,
• Extract, predict, and plot these data.

SWIRL:

• Binomial_Data

• Count_Data

Lab: Unit 4: Lab 3

Reading: Crawley, M. The R Book. Chs 13–17.

Functions: glm(), predict(), lines().

Lesson 4. How to deal with multiple response variables (multivariate data)

Lecture: Multivariate Data

Learning Goals:

• Understand multivariate data,
• Know how to analyse and plot multivariate data,
• Search for help on multivariate analyses.

More details:

• The vegan,
• and labdsv R packages.
• Material on ordination,
• New lectures by author of vegan.
• Manly and Alberto. 2017. Multivariate statistical methods: A primer. Fourth edition. Taylor & Francis.
• More examples here

SWIRL: Ordination

Lab: Unit 4: Recap

Functions: pcord, prcomp(), princomp(), pca(), biplot(), interp().