# Fit Polynomial Model to Data

This example shows how to fit a polynomial model to data using the linear least-squares method.

Load the `patients` data set.

`load patients`

The variables `Diastolic` and `Systolic` contain data for diastolic and systolic blood pressure measurements, respectively. Fit a third-degree polynomial to the data with `Diastolic` as the predictor variable and `Systolic` as the response.

`polymodel = fit(Diastolic,Systolic,"poly3")`
```polymodel = Linear model Poly3: polymodel(x) = p1*x^3 + p2*x^2 + p3*x + p4 Coefficients (with 95% confidence bounds): p1 = -0.001061 (-0.003673, 0.001551) p2 = 0.2844 (-0.3701, 0.9389) p3 = -24.72 (-79.2, 29.76) p4 = 821.1 (-685.5, 2328) ```

`polymodel` contains the results of the fit. Display the least-squares method used to estimate the coefficients by using the function `fitoptions`.

```opts = fitoptions(polymodel); opts.Method```
```ans = 'LinearLeastSquares' ```

The output shows that `polymodel` is fit to the data with the linear least-squares method. Evaluate `polymodel` at the values in `Diastolic`, and display the result together with a scatter plot of the blood pressure data.

`plot(polymodel,Diastolic,Systolic)`

The plot shows that `polymodel` follows the bulk of the data.