1. Why is it appropriate to use the variable “year event occurred” as the explanatory variable and “estimated year event occurred” as the response variable?

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.

2. What would the scatterplot look like if you had guessed the correct year for each event?

There would be a completely straight positive correlational line going acoss the chart.

1. Which variable did you use as the explanitory variable when relating the number of strikes and lockouts witht he percentage of the total labor force with the union memebership (in your first scatterplot)? Why?

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.

2. What type of association is there between the number of strikes and lockouts with the percentage of the total labor force with union membership

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.

3. Explain what your second scatterplot shows.

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.

4. What type of association is there between the variables you related in your second scatterplot, or are the variables not associated?

There is still a postivive correlation, however this one goes from being closely clustered to being spread out quite far.

1. Which aspects of the data does the bar graph help interpret?

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.

2. Is there an association between the variables in your scatterplot? Explain.

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.

1. Now that we see all the states, are there any trends or associations in the data? Explain.

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.

2. Are there any data points that appear to stand away from the rest of the data? If so, which one(s) and what makes them stand out.

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.

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