Linear and Nonlinear Regression in Python

[@rodras98]

This helped me a lot. Thank you so much!

[@Passco666]

Where I can find that exercise document, please?

[@stefanofedele4820]

That "extra" at the end of the tutorial is the covariance matrix. Is there anyone who can explain me how that matrix can be used to interpret the final data?

[@apm]

There is more info on the covariance information here: docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.curve_fit.html "extras" at 21:08 is the estimated covariance of params. The diagonals provide the variance of the parameter estimate. To compute one standard deviation errors on the parameters use perr =np.sqrt(np.diag(pcov)).

[@hemraj5893]

How to fit function with r^2

[@apm]

R^2 can be calculated with this package: scikit-learn.org/stable/modules/generated/sklearn.metrics.r2_score.html Check out Python gekko or curve_fit for the regression.

[@nikhilkartha6157]

$ pip install apm Requirement already satisfied: apm in c:\anaconda\lib\site-packages (0.0.1) Requirement already satisfied: pyyaml in c:\anaconda\lib\site-packages (from apm) (3.12) Requirement already satisfied: requests in c:\anaconda\lib\site-packages (from apm) (2.11.1) $ python nonlr.py Traceback (most recent call last): File "nonlr.py", line 5, in from apm import * ImportError: No module named apm

[@apm]

Try the following: pip install APMonitor Then use: from APMonitor.apm import * In your script. More information is available at apmonitor.com/wiki/index.php/Main/PythonApp I also recommend that you look at the new Gekko package: apmonitor.com/wiki/index.php/Main/GekkoPythonOptimization