Actual year event Occurred vs. Estimated year occurred
  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 reason why the variable "year event occurred" as the explanatory is bacause those were the exact year when the events occurred, and nothing could change. The variables "estimated year event occurred" were guessed subjectively, depends on the person, so "estimated year event occurred" is consdered as resoinse variable.
  2. What would the scatterplot look like if you had guessed the correct year for each event?
    If all the years were correctly guessed for each event, there would be only one dot for each event on the scatterplot, seems like one group data.
Union Membership Percentage vs. Strikes and Lockouots
Strikes and Lockouots vs. Union Membership Percentage
  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?
    In the first scatter plot, "Strikes and Lockouts" is the explanatory variable. It is reasonable that more workers stiked or lockouted, higher number of union membership would be made.
  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?
    The number of strikes and lockouts and the percentage of the total labor force with union membership have a roughly positive association.
  3. Explain what your second scatterplot shows.
    In the second scatterpolt, the "Strikes and Lockouts" is the response variable, which is depended on the percentage of the total force with union membership. As pecentage of the total force with union membership increases, the number of strikes and lockouts increases.
  4. What type of association is there between the variables you related in your second scatter plot, or are the variables not associated?
    Same with the first scateerpolt, there is a positive association between two variables.
Governors' Salaries and Per Capita Income in 9 states
Governors' Salaries vs. Per Capita Income
  1. Which aspects of the data does the bar graph help interpret?
    Bar graph interprets the comparing of governors' salaries and per capita income with specific number in each of 9 states.
  2. Is there an association between the variables in your scatterplot? Explain.
    There is no association between the two variables in the scatterpolt. Governors' Salary and per capital income do not depend on each other.
Governors' Salaries vs. Per Capita Income in USA in 2010
  1. Now that we can see all the states, are there any trends or associations in the data? Explain.
    There is no any kind of associations between the governors' salary and per captial income in all the states according to the scatterplot above.
  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.
    There are two points that appear to stand away from the rest of the data, (70000,37300) and (150000,56001). First point has a low governors' salary and second point has a much higher per captia personal income.
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