1. Extra Credit The following graphs illustrate why one needs to be cautious when implying causation. Although a graph of two variables may display strong correlation, we must be careful not to assume causation! Analyze each of the following graphs. For each one, discuss the distribution. Be certain to comment on any association you might see between the variables that is evident from the graph. For each graph, discuss any lurking variables that may be present that might influence this association and comment on the trouble with concluding causation. (total 5 extra credit points). For an additional 5 extra credit points, find a graph (be sure to cite your source) similar to the ones in this slide show that incorrectly imply causation between variables.

2. Obesity and Transportation Source: http://www.treehugger.com/files/2007/10/get_out_of_the.phphttp://www.treehugger.com/files/2007/10/get_out_of_the.php

3. Rock Music and Oil Production Source: http://www.overthinkingit.com/wp-content/uploads/2008/09/rs-500-us-oil-production1.jpghttp://www.overthinkingit.com/wp-content/uploads/2008/09/rs-500-us-oil-production1.jpg

4. US Highway Fatality and Imported Lemons Source: http://nerdnirvana.org/wp-content/uploads/2009/10/kfCmN.jpghttp://nerdnirvana.org/wp-content/uploads/2009/10/kfCmN.jpg

5. Autism and MMR Vaccine Source: http://www.medicine.ox.ac.uk/bandolier/booth/Vaccines/noMMR.jpg http://www.medicine.ox.ac.uk/bandolier/booth/Vaccines/noMMR.jpg

6. Pirates are Cool... Source: http://1.bp.blogspot.com/_BUNTMvRnBh0/R3zuBjogzFI/AAAAAAAAAAM/PU5b2wV9wqU/s400/piratesarecool.jpg