Actual vs Estimated Events Occured

  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?
    • Because the response variable, "estimated year event occurred", is depended on or is explained by the explanatory variable, "year event occurred". In other words, it is impossible to estimate the year event occured without knowing the approximate time period it occurred in.
  2. What would the scatterplot look like if you had guessed the correct year for each event?
    • The scatterplot would look less scattered because the x- and y- coordinates are the same.


Strikes and Lockouts in Labor Force (1950-2010)

Strike Scatterplot #2

  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?
    • The total labor force with union membership was used as the explanatory variable and the number of strikes and lockouts was used as the response variable. Because the number of strikes and lockouts depends on the total labor force with union membership.
  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 a positive association between the explanatory variable and response variable because as the union membership percentage increases, the more strikes and lockouts there are.

  3. Explain what your second scatterplot shows.
    • The second scatterplot shows strikes and lockouts as the explanatory variable and union membership percentage as the response variable. This scatter shows there is a directly proportional relationship between the variables. However, there are a few outliers.
  4. What type of association is there between the variables you related in your second scatter plot, or are the variables not associated?
    • There is a positive association between the variables.


Bar Graph: Governor-Small

Scatterplot: Governor-Small

  1. Which aspects of the data does the bar graph help interpret?
    • The bar graph helps to interpret and compare the individual state's per capita personal income and governor's salary.
  2. Is there an association between the variables in your scatterplot? Explain.
    • No, there isn't an association between the variables because the data points are scattered all around, thus, the two variables don't correlate.


Scatterplot: Governor-Large

  1. Now that we can see all the states, are there any trends or associations in the data? Explain.
    • No, there are no associations in the data since the data points are scattered all over the graph.
  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.
    • Maine, Wyoming, New York, Maryland, New Jersey, Massachusetts, and Connecticut are the states that appear to stand away from the rest of the data. These states' per capita personal income or governor's salary are either too high or too low.


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