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Coefficient of Determination (R-Squared)

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  • 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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Confidence Intervals (CI) for Dependent Variable Prediction

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Analysis of Variance or ANOVA

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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Statistical Methods