Inferential Statistics Essentials
The first-course path through classical inference: one- and two-sample mean comparisons, analysis of variance, association for categorical and continuous variables, then the bridge to regression.
Guided pathways
A pathway is an ordered route through the library: real, published method entries in the sequence a first course would teach them. Every step below links to the entry it names.
4 pathways27 method entries covered
Machine Learning Foundations
A first pass over the workhorse machine-learning methods: simple supervised classifiers, tree ensembles, and the two classic unsupervised tools (clustering and dimensionality reduction).
Multi-Criteria Decision Making (MCDM)
A guided path through the core multi-criteria decision-making methods: start with simple value aggregation, weight criteria with pairwise comparison, then meet the distance-to-ideal, compromise and outranking schools.
Time-Series Forecasting
A short path into univariate time-series modelling: test a series for a unit root (stationarity), then build and forecast with a Box-Jenkins ARIMA model.