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Multiple Regression and Coefficient of Determination (R-Squared)

CFA® Exam Level 2

This lesson is part 9 of 17 in the course Quantitative Methods
  • 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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‹ Multiple Regression Analysis

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In this Course

  • 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?

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