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STAT 200

Topic 1: 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 correlates with Per capita consumption of margarine Correlation: 99 26% (r=0.992538) 2000 2001 2002 2003 2004 2005 2005 2007 2008 2009 4 95 per 1,000 4 63 per 1003 Divorce race in Maine 4.29 per 1,000 3 96 per 1,000 2080 2002 2003 2804 2005 2007 Margarine consumed + Divorce rate in Maine Data sources: National Vital Statistics Reports and U.S. Department of AgricultureHere we see that, at least visually, there appears to be a relation between the divorce rate in Maine and the per capital consumption of margarine. Does this imply that all married couples in Maine should immediately stop using margarine in order to stave off divorce? Common sense tells us that is probably not true. This is an example of a spurious correlation in which there appears to be a relation between the divorce rate and margarine consumption but, it is not a causal link. The appearance of such a relation could merely be due to coincidence or perhaps another unseen factor