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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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Multiple Regression Analysis

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01
CFA L2: Quantitative Methods - Introduction
02
Quants: Correlation Analysis
03
Quants: Single Variable Linear Regression Analysis
04
Standard Error of the Estimate or SEE
05
Confidence Intervals (CI) for Dependent Variable Prediction
06
Coefficient of Determination (R-Squared)
07
Analysis of Variance or ANOVA
08
Multiple Regression Analysis
09
Multiple Regression and Coefficient of Determination (R-Squared)
10
Fcalc – the Global Test for Regression Significance
11
Regression Analysis and Assumption Violations
12
Qualitative and Dummy Variables in Regression Modeling
13
Time Series Analysis: Simple and Log-linear Trend Models
14
Auto-Regressive (AR) Time Series Models
15
Auto-Regressive Models - Random Walks and Unit Roots
16
ARMA Models and ARCH Testing
17
How to Select the Most Appropriate Time Series Model?

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