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?
2. The explanatory variable drives the result of the response variable. So by having the actual year as the explanatory variable it explains or gives the actual answer to the response varibale.
3. What would the scatterplot look like if you had guessed the correct year for each event?
4. If the estimated year and actual year would have been the same year for every event, the scatter plot would have been a straight line.
1. Which variable did you use as the explanatory variable when relating the number of strikes and lockouts with the percentage of the total labor force with union membership (in your first scatter plot)? Why?
2. In the fisrt scatterplot I used the number of strikes and lockouts as the explanatory variable when relating it to the percentage of union membership.
3. What type of association is there between the number of strikes and lockouts with the percentage of the total labor force with union membership?
4. There seems to be a positive association between the number of strikes and lockouts with the percentage of the total labor force with the union membership.
5. Explain what your second scatterplot shows
6. The second scatterplot shows the the percentage of the work force with a union membership over the years.
7. What type of association is there between the variables you related in your second scatter plot, or are the variables not associated?
8. There is a negative association between these two variables. Every year the union membership rate continually decreases.
1. Which aspects of the data does the bar graph help interpret?
2. It helps compare the state governor's salary to the per capita personal income of each state.
3. Is there an association between the variables in your scatterplot? Explain.
4. There is not a clear association between these two variables shown in the scatterplot.
1. Now that we can see all the states, are there any trends or associations in the data? Explain.
2. There is a slight positive association between the two variables, but I would not say that it is a strong positive association.
3. 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.
4. There isn't a point that stands out too much, but the data point for Maine is the one that is a little bit of an outlyer. Data point is (70000,37300) Back