Chapter 12. Multilevel Analyses

In Chapter 10, Classification with k-Nearest Neighbors and Naïve Bayes, we discussed association with k-Nearest Neighbors and Naïve Bayes. In the previous chapter, we examined classification trees using notably C4.5, C50, CART, random forests, and conditional inference trees.

In this chapter, we will discuss:

  • Nested data and the importance of dealing with them appropriately
  • Multilevel regression including random intercepts and random slopes
  • The comparison of multilevel models
  • Prediction using multilevel modeling

Nested data

If you have nested data, this chapter is essential for you! What is meant by nested data is that observations share a common context. The examples include:

  • Consumers nested within shops
  • Employees nested ...

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