## model.fit_aging_curve()


Fit a weighted quadratic in centered form.


Usage


``` python
model.fit_aging_curve(
    df,
    y_col,
    weight_col,
    age_col="Age",
    center=30.0,
)
```


Model: `y = A + B·(Age − center) + C·(Age − center)² + ε`

The centered parameterization gives each coefficient an interpretable meaning at `Age = center`:

- `A` -- predicted `y_col` at the centering age (default 30).
- `B` -- slope at the centering age.
- `C` -- curvature (negative for a true peak; the more negative, the sharper the peak and the steeper the post-peak decline).

With `C < 0` the closed-form peak and max are:

- `peak_age = center − B / (2·C)`
- `max_value = A − B² / (4·C)`

Returns `None` when fewer than 3 non-null rows are available.