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?

# Coefficient of Determination (R-Squared)

- Typically noted as R2yx or R-squared in the stats report.
- This value measures the percentage of variation in Y that is explained by the model and will be between 0 and 1 (and not to be confused with the Correlation Coefficient which will be between -1 and 1).
- Example: a coefficient of determination/R-squared = .80 would mean that 80% of the variation in dependent Y variable is explained by the model’s regression equation.
- For single variable/simple regression, the coefficient of determination equals the square of the data sample’s correlation coefficient.

The following video from Khan Academy explains the calculation of the Coefficient of Determination.

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