Regression, time-series interpretation, and evidence quality in Level II investment analysis.
Level II Quantitative Methods is less about carrying out a long calculation and more about reading what a model is actually telling you. Candidates usually lose points when they can recite a technique name but cannot judge whether the estimate, forecast, or test result is economically meaningful.
This chapter now starts with grouped lessons rather than a one-reading-per-page mirror. That is the stronger online shape because the exam usually blends model setup, diagnostic judgment, forecast interpretation, and data-method limits inside one vignette.
| Lesson | Official coverage boundary | What to focus on |
|---|---|---|
| Multiple Regression, Assumptions, and Coefficient Logic | Basics of Multiple Regression and Underlying Assumptions | Model setup, coefficient meaning, residual behavior, and what the assumptions do for interpretation. |
| Model Fit, Misspecification, and Regression Extensions | Evaluating Regression Model Fit and Interpreting Model Results + Model Misspecification + Extensions of Multiple Regression | ANOVA, goodness of fit, joint tests, prediction, heteroskedasticity, serial correlation, multicollinearity, and logistic or dummy-variable extensions. |
| Time-Series, Stationarity, and Forecast Choice | Time-Series Analysis | Trend models, AR processes, mean reversion, unit roots, seasonality, ARCH, RMSE, and choosing the right time-series framework. |
| Machine Learning, Overfitting, and Big Data Workflow | Machine Learning + Big Data Projects | Which algorithms fit which problems, how to control overfitting, and how a real data-analysis workflow changes the investment conclusion. |