Coding Categorical Variables In Regression: Indicator or Dummy Variables

In this video, I explain how to code categorical variables for use as explanatory variables (x-variables) in regression by using indicator or dummy variables. Categorical variables are very common in real data sets, so understanding how to create and use dummy variables is both very useful and important.

A pdf copy of the slides that are used in the video can be found here. The video also discusses an example which is implemented in Excel. The Excel file can be downloaded here.

The video is about 29 minutes

Bias vs. Variance Tradeoff, Cross-Validation, and Overfitting in Prediction (Part 2)

This is the second part of the two-part video series that discusses the bias vs. variance tradeoff, overfitting, and basic cross-validation.

The R source code used in this video can be found here.

Note: You will want to play this video at 1080p HD and full screen in order to be able to see what is going on.

Bias vs. Variance Tradeoff, Cross-Validation, and Overfitting in Prediction (Part 1)

This video discusses the bias vs. variance tradeoff, overfitting, and basic cross validation. There are two parts. Part 1 (below) discusses the ideas. Part 2 shows an example using regression trees.

A pdf file of the slides used in this video can be obtained here.