1. The explanatory variable drives the value of the response variable. As the explanatory variable varies, the value of the response variable changes in response. Because we are measuring the estimated dates in relation to the actual dates, it makes sense that if the actual date were to change, our percieved estimation would change as well in order to more closely match the accurate date.
2. If I had guessed the correct year for each date there would only be one set of points that match up with the actual dates. My accurate guesses would have fallen right on top of the actual dates creating one set of points.
1. I used number of strikes and lockouts as the explanatory variable because it is the independent variable.
2. As the number of Strikes and lockouts increases the union membership percentage increases.
3. The second scatterplot represents the number and strikes and lockouts per year.
4. The variables are roughly associated. The most common trend is that as we move closer towards present day, the fewer number of strikes and lockouts there are.
1. This data helps to compare personal income vs governor income by state in 2010.
2. There is no significant association between the two variables.
1. There is still no significant trend in the data.
2. No, most of the data is clustered together.
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