To determine the type of association shown in a scatterplot, you generally look for patterns in the data points. Here's how you can interpret the options provided:
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No Association: If the data points are scattered randomly without a discernible pattern, then there is no association.
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Non-linear Association: If the data points form a curved pattern (like a U-shape or any other non-linear trend) rather than a straight line, it indicates a non-linear association.
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Linear Association: If the data points roughly follow a straight line (either upward or downward), it suggests a linear association.
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Negative Association: If the points trend downwards from left to right (as one variable increases, the other decreases), that indicates a negative association.
Without seeing the specific scatterplot, I can’t definitively say which option you should choose. However, you can use the descriptions above to classify the scatterplot based on the observed trend of the data points. If you can provide additional details about the scatterplot, I'd be happy to help you analyze it further!