In the following section, we will see the method of finding the best linear fit for a set of data.
Finding the best linear fit for a set of data
Getting ready
This is the main requirement that we need to have:
numpy.polyfit(x, y, deg, rcond=None, full=False, w=None, cov=False)
How to do it...
This table represents the main parameters of the method:
| Parameters |
x: array_like, shape (M,). x coordinates of the M sample points (x[i], y[i]). y : array_like, shape (M,) or (M, K). y coordinates of the sample points. Several datasets of sample points sharing the same x coordinates... |
