In this chapter, we looked at linear, multiple, and logistic regression. We fetched data from online sources, cleaned it up, and mapped it to the data structures that we are interested in. The world of statistics is huge and there are numerous special areas even for these somewhat straightforward concepts and methods. For regression analysis, it is important to note that correlation does not always mean causation, that is, just because there is a correlation between two variables, it does not mean that they depend on one another in nature. There are websites that show these spurious correlations; some of them are quite entertaining ( http://www.tylervigen.com/spurious-correlations ).

In the next chapter, we will look at clustering techniques to find similarities in data. We will start out with an example using the same data that we saved in this chapter when performing multiple regression analysis.