1. Because variables of interest in an experiment (those that are measured or observed) are called response variables. Other variables in the experiment that affect the response and can be set or measured by the experimenter are called explanatory variables.
2. The dots will following as a F(x) = x linear function
1. I used numbers of strikes and lockouts as explanatory variables and used percentage of total labor force as response variables. Because numbers of strikes and lockouts is a independent variables that explain variability in the respones variables.
2. They are positive linear association
3. This scatter shows the relationship between how numbers of strikes and lockouts is changing as percentage of total labor force
4. Its positive linear association also
1. The difference between the two data of each entry of the data
2. They are almost positive linear association but not strightly followed.
1. Most likely the higher personal income leads to higher governor's salary in this states. But the trend is only in a range instead of association.
2. The dot on the very right in the scatter of Connecticut looks like standing out.
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