Fit Polynomial Model to Data
This example shows how to fit a polynomial model to data using the linear least-squares method.
patients data set.
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
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.
The plot shows that
polymodel follows the bulk of the data.