One goal of statistics is to identify relations among variables. What happens to one variable as another variable changes? Does a change in one variable cause a change in another variable? These questions can lead to powerful methods of predicting future values through linear regression. It is important to note the true meaning and scope of correlation, which is the nature of the relation between two variables. Correlation does not allow to say that there is any causal link between the two variables. In other words, we cannot say that one variable causes another; however, it is not uncommon to see such use in the news media. An example is shown below. Divorce rate in Maine E correlates with Per capita consumption of margarine Correlation: 99.26% (r=0.992558) 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 4.95 per 1,000 BIDS 4.62 per 1,000 lbs Margarine consumed Divorce rate in Maine 4.29 per 1,000 4lbs 2lbs 3.96 per 1,000 2001 2002 2004 2006 2007 2008 2009 2000 2003 2005 Margarine consumed + Divorce rate in Maine tylervigen.com Data sources: National Vital Statistics Reports and U.S. Department of Agriculture