Statistical Modeling in R

Description of Workshop

This workshop is part of the Data Science Workshops hosted by Research Computing Services at Northwestern University (2020).

The purpose of the workshop is to introduce students to statistical modeling using linear, logistic and Poisson regression.

Students are expected to have knowledge of R and regression analysis.

Packages

This workshop requires packages: readr, pscl, caret, stats.

Presentation

I will be lecturing while live coding. Links to the presentation are given below:

• Linear Regression
• Logistic Regression
• Poisson Regression

Code associated with Presentation

For the live coding portion, I will be using the following markdown files without code filled in:

• Linear Model
• Logistic Model
• Poisson Model

For access to markdown file with the code filled in, see the following links:

• Linear Model
• Logistic Model
• Poisson Model

Exercises

After the presentation, here are exercises that I wrote for attendees to get more practice.

Exercises without answers:

• Linear Model
• Logistic Model
• Poisson Model

Exercises with answers:

• Linear Model
• Logistic Model
• Poisson Model

The exercises should follow directly from the workshop.

Downloading Files

Recommended: Entire directory

You can download all of the files by clicking the green button ("Clone or download") above and choosing "Download ZIP."

Individual Files

If you download files from the links above, you have to click through to the RAW version of the R markdown files and download that. If you download directly from the links above, the files won't open because they are web pages, not the raw files.

Resources

See Resources for a listing of general R resources, tutorials, and reference materials. Links below relate specifically to material covered in this workshop.

Linear models in R: this is brief dicussion of linear models in R. There is also another tab on GLMs.

Statistical Formula Notation in R: notes on regression notation in R

GLMs blog series in R: this a link to the first of a seven part series on GLMs in R.