- #1

You are using an out of date browser. It may not display this or other websites correctly.

You should upgrade or use an alternative browser.

You should upgrade or use an alternative browser.

- MATLAB
- Thread starter SK1.618
- Start date

In summary, the attached PDF provides information on calculating errors for fit parameters using polyfit and polyval. The function polyfit returns polynomial coefficients and a structure S, which includes the triangular factor, degrees of freedom, and norm of the residuals. The function polyval uses this structure to generate error estimates, delta, which represents the standard deviation of the error in predicting a future observation. If the coefficients are least squares estimates and the errors in the data are independent, normal, and have constant variance, the predictions will fall within the range of y±delta at least 50% of the time. A helpful example can also be found on the polyfit page.

- #1

Physics news on Phys.org

- #2

kreil

Gold Member

- 668

- 68

[p,S] = polyfit(x,y,n) returns the polynomial coefficients p and a structure S for use with polyval to obtain error estimates or predictions. Structure S contains fields R, df, and normr, for the triangular factor from a QR decomposition of the Vandermonde matrix of x, the degrees of freedom, and the norm of the residuals, respectively. If the data y are random, an estimate of the covariance matrix of p is (Rinv*Rinv')*normr^2/df, where Rinv is the inverse of R. If the errors in the data y are independent normal with constant variance, polyval produces error bounds that contain at least 50% of the predictions.

[y,delta] = polyval(p,x,S) uses the optional output structure S generated by polyfit to generate error estimates delta. delta is an estimate of the standard deviation of the error in predicting a future observation at x by p(x). If the coefficients in p are least squares estimates computed by polyfit, and the errors in the data input to polyfit are independent, normal, and have constant variance, then y±delta contains at least 50% of the predictions of future observations at x.

There is also a good example on the polyfit page.

MATLAB is a software platform commonly used by scientists, engineers, and analysts to conduct data analysis, visualization, and algorithm development. It allows for easy manipulation of data, creation of complex mathematical models, and provides a user-friendly interface for programming.

To find errors in polynomial fit parameters using MATLAB, you can use the built-in function polyfiterror. This function takes in the experimental data, as well as the polynomial degree, and returns the estimated error in the polynomial fit parameters.

Yes, you can plot the errors of the polynomial fit parameters in MATLAB using the function errorbar. This function takes in the x and y values of the data, as well as the error values, and plots them as vertical lines on the data points.

MATLAB uses a statistical method called least squares to calculate the errors in polynomial fit parameters. This method minimizes the sum of the squares of the differences between the data points and the polynomial curve, and provides the best fit for the data.

If you encounter an error while using MATLAB, try checking your code for any mistakes or typos. You can also refer to MATLAB's documentation or seek help from the MATLAB community to troubleshoot the issue. Additionally, make sure you have the correct data format and parameters specified for the function you are using.

- Replies
- 12

- Views
- 3K

- Replies
- 5

- Views
- 986

- Replies
- 2

- Views
- 3K

- Replies
- 2

- Views
- 1K

- Replies
- 5

- Views
- 1K

- Replies
- 14

- Views
- 3K

- Replies
- 9

- Views
- 2K

- Replies
- 5

- Views
- 2K

- Replies
- 6

- Views
- 4K

- Replies
- 8

- Views
- 819

Share: