- It's appropiate because you get to see the change in your estimate from the actual value by seeing how far the point is from the line y = x.
- If the estimates were correct then the scatter plot would look like a straight line with a slope of 1.

- I used the Union Membership Percentage as the explanatory variable. I chose this because it seemed reasonable to expect that the percentage of union members would determine how many strikes and lockouts happened every decade.
- I feel like the percentage of union members directly relates positively to the amount of stirkes and lockouts.
- My second scatter plot shows how this information will look when we switch the variables.
- As you can see, the relationship between the two variables with strikes and lockouts as the explanatory variable shows a positive direct relationship as well.

- The bar graph helps us visualize the difference between the personal income per capita and the Governor's salary by state.
- There seems to be a slight positive correlation between the two variables but it's not that great therefore I would not be confident presenting it as such.

- There still seems to be a positive coreelation but once again it isn't that great of a correlation..
- There seems to be only one piece of data (Connecticut) that is highest, but other than that, almost all the data is clustered.