To accurately interpret the scatterplot and determine the type of association it shows, I would need to see the visual representation of the data. However, I can explain how to interpret the associations based on common scenarios:
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Positive Association: If the points on the scatterplot tend to rise from left to right, it indicates that as one variable increases, the other variable also tends to increase.
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Negative Association: If the points tend to fall from left to right, it shows that as one variable increases, the other variable tends to decrease.
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Linear Association: If the points form a clear line (either upward or downward), it indicates a linear relationship, meaning the relationship can be well described by a straight line.
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Non-linear Association: If the points do not form a straight line (they might form a curve or another pattern), it indicates a non-linear relationship.
Based on your options, here’s how to select the correct association:
- If you see a line with a positive slope, choose "The graph shows a positive association."
- If you see a line with a negative slope, choose "The graph shows a negative association."
- If the points are scattered but still form a recognizable linear pattern, you might say "The graph shows a linear association."
- If the data points do not fit a linear pattern and instead show some curvature or another form, choose "The graph is non-linear."
Without the actual scatterplot data presented, these guidelines should help you assess the association represented in the scatterplot.