Non-Parametric Models: Splines

The total length of the videos in this section is approximately 6 minutes. You will also spend time answering short questions while completing this section.

You can also view all the videos in this section at the YouTube playlist linked here.

This lecture marks a transition. We have been focusing on methods for comparing two groups (randomization/permutation tests, t-tests). Now we are moving on to methods for examining the relationships between multiple variables, with one or more predictors and an outcome variable. As before, there will be non-parametric methods and parametric methods. Splines are non-parametric.

Introduction to splines

Question 1: Suppose that you connect all of the dots in the video's example. You use the resulting connected line as your model for predicting Y given X. So, for example, if your original data set included data (10,12), then the model predicts that data points with X=10 will have Y exactly equal to 12. Is this a good model?

Details

Question 2: When might you use a spline? Check all that apply.

• When you want to test a hypothesis

• When you want to make predictions

• When you want to estimate a slope

Conclusion

Question 3: Try to explain the phrase "model for the mean."

Short and sweet.

During this tutorial you learned:

• How to predict mean of Y given X, using a spline model

Terms and concepts:

Spline, overfitting, model for the mean