Sample moments, the Central Limit Theorem (CLT), and hypothesis testing (z-tests and t-tests).

Recent additions include Machine Learning (ML) methods such as neural networks, decision trees, and regularization (LASSO/Ridge). 2. Strategic Focus for Page 1 & Chapter 1

Stationary and non-stationary series (AR, MA, ARMA models) and simulation techniques like Monte Carlo and bootstrapping.

As you begin with the first reading ( Fundamentals of Probability ), prioritize these high-yield concepts that GARP frequently tests:

Distinguishing between independent events (where the occurrence of one doesn't change the probability of the other) and mutually exclusive events (which cannot occur at the same time).

Fundamentals of probability, random variables (discrete and continuous), and common univariate/multivariate distributions.