Returns, discounting, statistics, inference, regression, and data techniques tested in Level I investment problems.
Quantitative Methods gives Level I its numerical language. The exam is rarely testing whether you can push buttons faster than everyone else. It is usually testing whether you can choose the right return measure, read a probability statement correctly, interpret a test result, or see what a regression output actually implies.
That is why this chapter is grouped into a few dense lessons instead of one page per learning outcome. The goal is to keep the ideas connected: discounting flows into valuation, statistics flows into portfolio thinking, and inference flows into how you judge evidence.
| Lesson | Official module coverage boundary | What to focus on |
|---|---|---|
| Returns, Time Value, and No-Arbitrage Logic | Rates and Returns; Time Value of Money in Finance | Return conventions, discounting, implied rates, and why cash flows add even when returns do not. |
| Statistics, Probability, and Portfolio Math | Statistical Measures of Asset Returns; Probability Trees and Conditional Expectations; Portfolio Mathematics; Simulation Methods | Central tendency, dispersion, conditional reasoning, diversification math, and scenario generation. |
| Inference and Hypothesis Testing | Estimation and Inference; Hypothesis Testing; Parametric and Non-Parametric Tests of Independence | Sampling logic, test decisions, p-values, and what independence questions are really asking. |
| Regression and Big Data Basics | Simple Linear Regression; Introduction to Big Data Techniques | Regression output interpretation, assumptions, and the Level I version of data-science vocabulary. |