Regression Fitting & Plotting
Enter independent variable x and dependent variable y, and it automatically fits linear, quadratic, and cubic polynomial regressions, compares each model's R² and adjusted R², selects the best model by adjusted R², and plots it. Useful for analysing trends such as how ADC changes with gestational age.
① Input data
One data point per row, format: x (e.g. gestational age) y (e.g. ADC value), separated by space, Tab, or comma. You can paste two columns from Excel.
Mode d'emploi et méthodologie
What is this tool good for?
It suits analysing how one continuous measure changes with another continuous variable, e.g. how fetal brain-region ADC changes with gestational age. The tool fits linear, quadratic, and cubic models simultaneously to help judge whether the trend is a straight line or a curve.
What is the difference between R² and adjusted R²?
R² (coefficient of determination) reflects how well the model fits the data — the closer to 1, the better; but raising the model's degree always increases R², which can cause overfitting. Adjusted R² penalises model complexity and is better for comparing models of different degrees, so this tool selects the best model by adjusted R².
Is the model with the highest R² always best?
Not necessarily. A high-order model's high R² may just be overfitting noise and need not be more biologically reasonable. Prefer a model that fits biological laws, has a good adjusted R², and is simple in form, rather than blindly chasing the highest R².
How do I prepare the data?
One data point per row, format 'x value + y value' (e.g. GA + ADC value), separated by space, Tab, or comma; you can paste two columns from Excel. At least 3 points are required, and at least 4 are recommended for fitting a cubic model.
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