Term: R-Squared
Type: Statistical measure
Used in: Regression analysis, finance, data science
Purpose: Measures how well data fits a model
Definition
R-Squared, or R², is a statistical metric that shows how much of the variation in a dependent variable can be explained by the independent variable(s) in a regression model. It’s expressed as a value between 0 and 1 (or 0% to 100%).
In finance, R² helps evaluate how well a portfolio’s returns correlate with a benchmark index. In data science, it helps assess the accuracy of predictive models.
Key Features
- Range: 0 to 1 (higher = better model fit)
- R² = 0: Model explains none of the variance
- R² = 1: Model explains all the variance
- Used In: Linear regression, portfolio analysis, predictive modeling
- Also Called: Coefficient of determination
Common Use Cases
- Evaluating investment performance vs. an index
- Measuring the accuracy of regression models
- Comparing predictive algorithms in data science
- Determining if independent variables are meaningful
- Academic and business research analysis
Benefits or Advantages
- Quantifies model reliability
- Easy to interpret as a percentage of explained variance
- Commonly accepted in statistics and finance
- Helpful in detecting overfitting or underfitting
Examples or Notable Applications
A mutual fund with R² = 0.95 is highly correlated to the S&P 500. A predictive model with R² = 0.30 may not be reliable. Regression analysis in Excel or Python includes R² as default output.
External Links
This post is for educational purposes only and does not constitute investment or statistical advice.