Matlab program for polynomial regression

Query points, specified as a vector. The points in x correspond to the fitted function values contained in y. If x is not a vector, then polyfit converts it into a column vector x :. Warning messages result when x has repeated or nearly repeated points or if x might need centering and scaling.

Fitted values at query points, specified as a vector. The values in y correspond to the query points contained in x. If y is not a vector, then polyfit converts it into a column vector y :. Degree of polynomial fit, specified as a positive integer scalar. Least-squares fit polynomial coefficients, returned as a vector.

Use polyval to evaluate p at query points. Error estimation structure. This optional output structure is primarily used as an input to the polyval function to obtain error estimates. S contains the following fields:. Centering and scaling values, returned as a two-element vector. These values center the query points in x at zero with unit standard deviation.

In problems with many points, increasing the degree of the polynomial fit using polyfit does not always result in a better fit. High-order polynomials can be oscillatory between the data points, leading to a poorer fit to the data. In those cases, you might use a low-order polynomial fit which tends to be smoother between points or a different technique, depending on the problem. Polynomials are unbounded, oscillatory functions by nature. Learn About Live Editor.

Select a Web Site. Choose a web site to get translated content where available and see local events and offers. Based on your location, we recommend that you select:. Select the China site in Chinese or English for best site performance. Other MathWorks country sites are not optimized for visits from your location. The function Fit implements least squares approximation of a function defined in the points as specified by the arrays x i and y i. The implementation is straightforward.

First the plane matrix A is created. The result c j are the coefficients. The code listed below is good for up to data points and fits an order-5 polynomial, so the test data for this task is hardly challenging!

Implementation the examples here helped alot to code this quickly :. Uses the routine lsqr A b from Multiple regression and mtp A from Matrix transposition.

Least squares solution using QR decomposition and package go. Uses module Matrix. Note that this implementation does not use floating point numbers, so we do not introduce floating point errors. Using exact arithmetic has a speed penalty, but for small problems like this it is inconsequential.

The above solution fits a polynomial of order Commented: Walter Roberson on 21 Apr So, I've completed my homework assignment and it works great! But my Professor said that I couldn't use polyval or poly fit for Polynomial Regression. So, I've started a new code, and I understand what to do to make it work the long way i. I'm sure it's an easy code to make, but I've spent a couple days digging through the internet and pretty much everything tells me to use the poly functions.

My original code is the first set of code, my new code Walter Roberson on 21 Apr Cancel Copy to Clipboard. Thats what I needed, got the linear one done in seconds. Just gotta work out a few things to get the quadratic and cubic, but thanks a lot!