This week's class first covers the basics of maximum likelihood and how various fit functions are calculated and used. This section includes caveats about what is assumed in ML and what can go wrong. Pay attention to the particularly interesting violation of assumptions that leads to the conclusion that every brown haired male in the U.S. is President Obama.
We next examine some methods for checking ML assumptions in your data. There are two R example scripts that run some graphical diagnostics and show how data transformations can be accomplished.