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Fit Polynomial Model to Data

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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)

Figure contains an axes object. The axes object with xlabel x, ylabel y contains 2 objects of type line. One or more of the lines displays its values using only markers These objects represent Data, Fitted curve.

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

See Also

Functions

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