I have data that depends on 4 independent variables (x1,x2,x3,x4) and I need a model (available in Python) to evaluate f(x1,x2,x3,x4) outside the data points. In principle, if I set 3 of my variables as constant values I can always use a polynomial fit of a reasonable degree (<5) to interpolate the data in the remaining dimension so I would
Tag: data-fitting
Polynomial fitting with equal number of data points and coefficients
I am currently experimenting with polynomial fitting using jupyter. The function below returns the least-square polynomial of degree m given the data points in xs with corresponding ys. Suppose I have the following six data points and fit a polynomial of degree 5: From my understanding, the resulting curve should pass through every single data point exactly (in fact, the
Is there a way to get the error in fitting parameters from scipy.stats.norm.fit?
I have some data which I have fitted a normal distribution to using the scipy.stats.normal objects fit function like so: I would now like to extract the uncertainty/error in the fitted mu and sigma values. How can I go about this? Answer You can use scipy.optimize.curve_fit: This method does not only return the estimated optimal values of the parameters, but