Questions:

Q7. What variables does this dataset contain?
This dataset contains: shape, several identifiers (STATEFP, STATENS, AFFGEOID, GEOID), state abbreviation, name, and area of land and water. The data set I added had names and populations for the states.

Q8. What classification methods did you use? How does each classification method bias the interpretation of the data?
For the first map, I used an equal interval classification with 8 classes. For the second map, I used a quantile classification with 8 classes. For the third and final map, I used natural breaks classification with 8 classes. I used 8 classes for each because I felt it best represented the scope of the data. Each classification method has different ways of interpreting the data. The equal interval split each of the classes in a way that showed more light-colored classes, while the natural breaks split the classes in a way that had more dark classes. Because each method splits the data differently, even with the same amount of classes, the data it creates differing amounts of darker vs. lighter areas. For example, in the equal interval classification map, the state of Texas isn’t the darkest color (most populated), while in the quantile map it has the darkest (most populated) classification.