To analyze the statements provided in relation to the correlation between a Major League Baseball team's spring training dates and the amount of rainfall in that city:
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The strength of the correlation between the two quantities is probably hard to determine unless given the rainfall amounts.
- True: Correlation strength is often assessed with the actual data points, so knowing the specific rainfall amounts would help in determining the correlation more accurately.
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High amounts of rainfall being normal for the area during springtime could be a variable influencing the correlation.
- True: High rainfall amounts being normal for the area could explain the observed correlation, indicating that the correlation may be due to underlying climatic conditions rather than a direct cause-and-effect relationship.
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A value of -9.84 could be a reasonable slope for the function equation.
- False: A slope of -9.84 implies a very sharp decrease, which would be unusual without any context on the scale of rainfall and training dates. It does not seem reasonable based on the context provided.
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There is causation between the spring training dates and the amount of rainfall in that city during the same period.
- False: Correlation does not imply causation. Just because there is a correlation between spring training dates and rainfall does not mean that one causes the other.
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A value of r = .79 is a possible measurement for the correlation coefficient of this association.
- True: An r value of .79 indicates a strong positive correlation, which is plausible in the context of the correlation mentioned.
In summary, the true statements are:
- The strength of the correlation between the two quantities is probably hard to determine unless given the rainfall amounts.
- High amounts of rainfall being normal for the area during springtime could be a variable influencing the correlation.
- A value of r = .79 is a possible measurement for the correlation coefficient of this association.
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