Interpreting R² and Confidence in Extrapolation
Extrapolation Calculator Team
When you use the Extrapolation Calculator, each result includes two important metrics: the R² score and the confidence percentage. Understanding these values is crucial for making informed decisions based on your extrapolations.
What is R²?
R² (the coefficient of determination) measures how well the regression line fits the observed data. It ranges from 0 to 1:
- R² = 1: Perfect fit — the model explains all variance in the data
- R² = 0: No fit — the model explains none of the variance
- R² > 0.7: Generally considered a good fit
- R² < 0.3: Suggests the model is a poor fit
Confidence Metric
The confidence percentage in our calculator is derived from the R² value and represents how reliably the model fits the data pattern. A higher confidence means the extrapolation method you selected aligns well with your data’s trend.
Choosing the Right Method
Comparing R² scores across different methods can help you choose:
- Try multiple methods on the same dataset
- Compare their R² scores
- Select the method with the highest R²
- Consider whether the method’s assumptions match your data’s nature
Important Caveats
- A high R² doesn’t guarantee accurate extrapolation — it only measures fit quality within observed data
- Extrapolation always carries more uncertainty than interpolation
- Physical or logical constraints should always override statistical predictions