10a: The year the event occured should be the true value, and the bases for comparison of the values. Therefore, this variable should be on the horizontal axis
10b: If each event was guessed correctly, the scatter plot would look like a perfect line with a slope of 1.
17a: The first plot here has "Union Membership" as the independent variable. This is because it seems like the the number of strikes and lockouts would depend on the union membership. The membership percent is independent of the strikes/lockouts, but the strikes/lockouts could realte to how many workers are in a union
17b: As the union membership percent increases, the strikes and lockout numbers increases.
17c: The second plot show that the union membership percent is dependent on the number of strikes/lockouts
17d: The second plot shows a similar association between the two variables, but it's not as clear as the first plot.
24a: The bar graph helps interpret the amounts per state, since the state is given on the x-axis
24b: The scatter plot shows a general trend that as personal income increases, the govenor's salary increases. There are 2 larger clusters where the personal income stays about the same, and the govenor's salary still increases.
31a: To me, there is not a clear trend between the per captia income and the govenor's salary. The data is very spread out with no distinct shape or trend. In general an increase of income gives an increase in the govenor's salary. But this is not ture for all states. This scatter plot shows there is no direct/strong relationship between the income and salary.
31b: points that stick out is where the per capita income is low but the salary is high, and vice versa. It is also interesting to see a wide range for the salary when the per capita income keeps about the same value.