More on Linear Regression

I return to the linear regression problem from last video. There isn’t any coding in this one, but I want to make sure we are thinking of curve fitting as an optimization problem. First we will visualize the least squares error function. Then we will derive the equations for the slope and intercept using basic calculus. Lastly, we will take our derived equations and plug them, along with the raw data from the last video, into Excel and compare the results to what we got using Python’s built in linear regression routine.

I will put a PDF of the derivation up on the Github repo. That should be available by the end of the week this is uploaded. I will also include the MATLAB code used to plot the error function as well as the Excel sheet.

Previous video on linear regression: https://youtu.be/jmKfDvk4k6g
Github: https://github.com/kpmooney/numerical_methods_youtube/tree/master/beta

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