The reason that the "year the event occurs" is the explanitory variable is because this is the set of data that influences the response variable or in this case the year you choose for the estimated year.
If a person estimates the same year on the chart as the actual year the points would be on top of each other appearing to be one point.
I chose to make "strikes and lockout" my explanatory variable because I felt like the number of strikes the union had was based on the amount of people, or the percentage of union members. If there are a lot of unhappy members, then it would lead to more strikes.
There is a positive association between the strikes and lockouts and the membership percentage because as the member percentage decreased the number of strikes decreased.
My second scatterplot shows the number of strikes and lockouts that happened per year.
There is a negative association because as the year changed or increased, the number of strikes and lockouts decreased.
The bar graph helps to compare the data based on the exact state.
There is no association between the two scatterplot variables because there is no clear trend that is shown between the two.
I still don't see any major trend, but with states that had a lower personal income, the governor would have a lower salary.
The state of Maine's point on the graph really stood out because its data values were significantly lower than the rest of the data, so the point was standing on it's own. Along with this state, the state of Connecticut's stood out because even thought the governor's salary was average, the capita of personal income was higher than the other states.