Quants: Single Variable Linear Regression Analysis- This is the “least squares” method.
- Situational Example:
- “If a country’s broad stock i...

Lessons

- 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?

# Multiple Regression and Coefficient of Determination (R-Squared)

- For a multiple regression model, this value represents the percentage of total variation in Y that is explained by the regression equation.
- The value is between 0 and 1.
- R-squared has a mathematical relationship with TSS, SSE, and RSS.
- R2 = RSS/TSS = (TSS-SSE)/TSS = 1- (SSE/TSS)
- The coefficient of determination alone does not indicate that a model is well specified, for example you could have more independent variables than necessary and the R2 will still be high – in this case your model would be not be considered parsimonious.
- Adjusted R2 = an alternate measure and will always be smaller than R2

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