We've built a foundation. Now to stats!

About 80-90% of any data analysis project is doing the data cleaning. Once you've confirmed that your data is cleaned properly and is tidy, now you can start trying some simple statistics on your data.

We're going to use broom, which is another package available in the tidyverse. broom makes the output and results of statistical models tidy, and easy to extract. We'll try to leverage all our skills to put together a simple data story given our data.

Note that we can't cover all of statistics in this unit. But we can show you some of the basic operations. Remember, if you don't completely understand the statistics, it's best to consult with a Biostatistician.

Our questions of interest are the following:

• Is mercury exposure greater within people who are fishermen versus those who are not? (t-test)
• What are the best predictors of mercury exposure? (multiple linear regression)