Seriously. I will need it for work. I haven’t been able to understand that shit from any online resource until now. Please ELI5
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I visited this reddit to get the Crypto crash explained to me but I saw a stats question!
Instead of super kid-friendly talk, I tried to make a more visual explanation. [https://imgur.com/a/2yUPjB2](https://imgur.com/a/2yUPjB2)
Let error be the distance between a data point and a prediction line. If we turn those distances into squares, bigger distances become bigger squares. A distance of 2 becomes an square of 4, a distance of 4 becomes a square of 16. This means that bigger distances (bigger errors) become a bigger problem. **You might think of the error lines in the picture as springs and more stretched out springs pull even harder on the line.** So that’s error and squared error. Now let’s add up all the squared errors and call that the “variance” (the total amount of spring pull).
When we do a line of best fit model, there is a default line that is always available. That is the average value. Let’s compute that variance and call it the “total variance”. Now let’s plug in our line of best fit model and compute its variance. That is the leftover error that we have not explained.
We can ask how much the total variance changed. The variance explained is the total variance minus the leftover variance. Now, compute the fraction variance explained / total variance. That is the proportion of variance explained, R-squared. It’s how much total spring pull in the first picture disappeared when we use the line the second picture.
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