How Random Forests improve simple Regression Trees?

In this post I am going to discuss some features of Regression Trees an Random Forests. Regression Trees are know to be very unstable, in other words, a small change in your data may drastically change your model. The Random Forest uses this instability as an advantage through bagging (you can see details about bagging here) resulting on a very stable model. The first question is how a Regression Tree works. Suppose, fore example, that we have the number of points scored by a set of basketball players and we want to relate it to the player's weight an height.