We should use the "Actual Years Event Occurred" as the explanatory variable because when determining the "Estimated Year an Event Occurred" we base our answer of the actual. This is due to knowing historical events, or being able to guess the age of a person.
If I had correctly guessed each year, then all the dots with the corresonding event would have perfectly lined up together therefor creating a postive linear association.
For the Union Membership data, I had the Union membership as the explanatory variable and the number of strikes for the response variable. I chose this because I felt as though the union would inspire the workers to stand up for their rights.
There is a positive association; as the union membership increases, so does the number of stikes and lockouts.
My second scatterplot has Strikes and Lockouts as the explanatory variable. This depicts the same information as in the other chart, however at a smaller incline. As the strikesa and lockouts increase, so does union membership.
In the second chart there is a small positive association.
The bar chart helps the viewer see the states that are in question, and how the governor's salary is dependant upon the per capita income of that state in particular.
The associaton between the variables in the scatterplot is relatively positive. Meaning as the per capita income increases, so does the governor's salary.
The noticable trends would include being able to see that the governor typically has a higher salary in states with a higher per capita income.
A few points stand out from the rest because the governor's salary for the state in question is higher than those where a similar per capita income takes place.