The explanitory variable is what was being tested based on the time frame that we thought the event happened we guesstimated the year in which we think it happened making that more like the dependant variable or response variable.
There would be a completely straight positive correlational line going acoss the chart.
For most charts the y axis is used as the dependent variable, or the explanatory variable, therefore for my first chart the Union Membership percentage would be classified as the explanatory variable.
There is an overall positive correlation between the number of strikes and lockouts compared to the union membership percentage. It als forms a more straight line showing that they are changing at the same rate most of the time.
The second scatterplot, still shows the mutual change in union membership and the number of strikes and lockouts. However, based on the y-axis Strikes and lockouts are the explanatory variable.
There is still a postivive correlation, however this one goes from being closely clustered to being spread out quite far.
The bar graph helped to show the difference between the states with the amount of money the everyday people made vs the Governor's salary.
There is no association between the two because it is just one large clusted of dots there is not a figurative line that can be made.
Now that the information for the other states was provided, there is still a large cluster, However, a tail is beginning to form to show a positive correlation.
The data points for Connecticut and Maine stand out from the majority of the cluster. Connecticut had a value much larger than the average for a personal income while Maine had a lower income than the rest for the Governor's Salary.