For r-squared, you will get a value that ranges from 0 to 1. Now 0 means your fit is terrible. It doesn't capture any of the variance in your data. While 1 is a perfect fit, where all of the variance in your data gets captured by this line, and all of the variance you see on either side of your line should be the same in that case. So 0 is bad, and 1 is good. That's all you really need to know. Something in between is something in between. A low r-squared value means it's a poor fit, a high r-squared value means it's a good fit.

As you'll see in the coming sections, there's more than one way to do regression. Linear regression is one of them. It's a very simple technique, but there are other techniques as well, and you can use r-squared as a quantitative measure of how good a given regression is to a set of data points, and then use that to choose the model that best fits your data.