# Detect highly correlated points between two signals given a linear-regression model

So I am faced with a problem for which I can't find a simple solution. I have attached an image of my basic explanation of the problem.

In the above image, we have two signals that have portions of high correlation and portions of low correlation. You can observe that the highly correlated points fall on this line with slope 1. I want to identify those points via some linear-regression model. So the idea is as follows:

Given a red line with a perfect slope (=1), we have the following linear-regression model

$$y_{MODEL} = mx + b + \epsilon$$

Here, $$\epsilon$$ is the error bounded by 95% confidence interval of this model.

Now, given two signals, $$S_1$$ and $$S_2$$, I want to see whether they fall within this model (and specifically) fulfill the criteria of being within 95% of the regression data confidence intervals. My problem is I don't know how I can fit a linear regression model to $$S_1$$ and $$S_2$$.

In theory, what I'd like to do is apply some type of constraint optimization that takes some coupled value of $$S_1$$ and $$S_2$$, sees which of those falls within the 95% bounds of $$y_{MODEL}$$.

I'm also not against using a whole different method, as long as I can find the highly correlated points between $$S_1$$ and $$S_2$$. I'd appreciate any guidance with this and it would be very helpful if I can get some reference to some code (I'm doing this in MATLAB) but Python is fine too.

import numpy as np