Updated on June 11, 2024Table of Contents
R-squared (R²) is a measure of how much variability in the dependent variable is explained by the independent variables in a regression model. It is a coefficient of determination that ranges from 0 to 1, with values closer to 1 indicating a better fit of the model to the data.
R² = 1 - (SSres / SStotal)
R² = 0.85
This means that the model explains 85% of the variability in the dependent variable.
What does R-squared tell you?
R-squared tells you the proportion of the variance in the dependent variable that is explained by the independent variables in a regression model. It measures the model’s goodness-of-fit.
How do you interpret R-squared?
R-squared is expressed as a percentage, indicating how well the model explains the variation in the data. For example, an R-squared of 0.7 means 70% of the variance in the dependent variable is explained by the model.
What is a good R-squared value?
A “good” R-squared value depends on the context and field, but generally, a higher R-squared value (closer to 1) indicates a better fit. In some fields, values above 0.7 are considered strong, while lower values may still be acceptable in others.
Is a higher R-squared better?
Yes, a higher R-squared is generally better as it means the model explains more of the variance in the data, indicating a better fit.
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