- CFA L2: Quantitative Methods - Introduction
- Quants: Correlation Analysis
- Quants: Single Variable Linear Regression Analysis
- Standard Error of the Estimate or SEE
- Confidence Intervals (CI) for Dependent Variable Prediction
- Coefficient of Determination (R-Squared)
- Analysis of Variance or ANOVA
- Multiple Regression Analysis
- Multiple Regression and Coefficient of Determination (R-Squared)
- Fcalc – the Global Test for Regression Significance
- Regression Analysis and Assumption Violations
- Qualitative and Dummy Variables in Regression Modeling
- Time Series Analysis: Simple and Log-linear Trend Models
- Auto-Regressive (AR) Time Series Models
- Auto-Regressive Models - Random Walks and Unit Roots
- ARMA Models and ARCH Testing
- How to Select the Most Appropriate Time Series Model?

# CFA L2: Quantitative Methods - Introduction

The title of this section really just says “applied statistics for financial analysis.” Some of the basic principles in the quant section will appear in other exam sections, and this series will attempt to highlight such items.

There will likely be one item set (six questions), but two is a possibility. Try not to let statistics un-necessarily weigh on your test pass strategy. Depending on your background, you may have seen some or all of this material; former students of undergrad economics or MBA programs should have some prior exposure.

**Material**

The material covered in this section includes:

- Correlation and Single Variable Regression Analysis
- Multiple Variable Regression Analysis
- Time Series Analysis

The profession of financial analyst requires a basic ability to create theories about relationships and testing those theories. For example, can one or more economic indicators predict the next twelve months return generated by a stock market index? An analyst may seek to find a statistically valid relationship ahead of his peers, and exploit this knowledge in an attempt to generate “excess returns” (as that concept is defined in a given investment situation).

**Tips:**

- If you have never seen a model output report from some commercial stats software, then you will need to take time to get comfortable with the format of cross sectional regression and time series model reports. Your item set questions may center on successfully interpreting an example stats report.
- The general guidance is to focus first on understanding the applied concepts (interpreting model outputs and identifying potential model pitfalls) and then incorporate the math and try to memorize some key formulas.
- Like every exam section, practicing problems is a must. If you are in the conceptual learning stage for quant, then you can read and try to work conceptual/interpretive questions in practice item sets, and return later to those same item sets to test your hand at the “pure math.”
- Candidates with limited stats experience can learn enough to pick up two or three points in a six point item set.

## Free Guides - Getting Started with R and Python

Enter your name and email address below and we will email you the guides for R programming and Python.