To accurately determine which statements are true based on the scatter plot, I would need to visually analyze the data. However, I can provide guidance on how to evaluate those statements based on typical characteristics of scatter plots:
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Linear Association: Check if the points approximate a straight line. If they do, you can conclude there is a linear association; otherwise, if the points form a curve or have no discernible pattern, it suggests non-linearity.
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Non-linear Association: If the points curve or otherwise do not form a straight line, then there is a non-linear association.
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Positive/Negative Association:
- A positive association means that as one variable increases, the other also tends to increase.
- A negative association indicates that as one variable increases, the other tends to decrease.
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Strength of Association:
- A strong association means the points are closely clustered around a line (either linear or curved).
- A moderate association indicates some spread but still shows a clear pattern.
- A weak association features points that are widely scattered with little to no visible pattern.
Based on those criteria, review the scatter plot and select the appropriate statements. If you see a linear pattern and the points are closely clustered, you might choose “There is a linear association” and possibly “The pattern of association is strong.” If the points are scattered and do not follow a clear direction or trend, it may point toward a weak association or no association.
If you can describe the scatter plot further or provide specific data points, I can help you deduce the correct statements more accurately!