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Association rule mining is a machine learning technique designed to uncover associations between categorical variables in data. It produces association rules which reflect how frequently categories are found together.
Bootstrapping—resampling data with replacement and recomputing quantities of interest—lets analysts approximate sampling distributions for complex estimators and frees them of the reliably unmet assumptions of traditional, parametric inferential statistics.
Logistic regression is a flexible tool for modeling binary outcomes. A logistic regression describes the probability, \(P\), of 1/“yes”/“success” (versus 0/“no”/“failure”) as a linear combination of predictors:
\[log(\frac{P}{1-P}) = B_0 + B_1X_1 + B_2X_2 + ... + B_kX_k\]
In R, “growing” an object—extending an atomic vector one element at a time; adding elements one by one to the end of a list; etc.—is an easy way to elicit a mild admonishment from someone reviewing or revising your code.
The term “multilevel data” refers to data organized in a hierarchical structure, where units of analysis are grouped into clusters. For example, in a cross-sectional study, multilevel data could be made up of individual measurements of students from different schools, where students are nested within schools.