R-Squared
R-Squared
R-Squared, or the coefficient of determination, is a statistical measure that represents the proportion of the variance for a Dependent variable that’s explained by an inDependent variable or variables in a Regression model. R-Squared values range from 0 to 1, where 0 indicates that the inDependent variable does not explain any variability in the Dependent variable, and 1 indicates that it explains all the variability.
Examples
- Simple Linear Regression: If you have a model predicting house prices based on square footage and the R-Squared value is 0.85, it means 85% of the variance in house prices can be explained by the square footage.
- Multiple Linear Regression: In a model predicting student performance based on study hours, attendance, and previous grades, an R-Squared of 0.90 indicates that 90% of the variance in performance can be explained by these factors.
Cases
- High R-Squared: An R-Squared of 0.95 in a climate model suggests a strong relationship between carbon emissions and temperature increase, indicating the model is likely capturing key trends.
- Low R-Squared: An R-Squared of 0.10 in a model predicting sales based on advertising spend might indicate that other factors (like market conditions or product quality) are more significant in explaining sales variability.