To determine which data point would cause the interpretation of the data set to be understated, we need to consider what "understated" means in this context. An understated interpretation would suggest that the trends or relationships in the data are not as strong or significant as they truly are.
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(20, 1): This point indicates that at a low temperature (20°F), there are only 1 hour spent at the pool. This data point is likely consistent with the trend where lower temperatures result in fewer hours at the pool.
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(55, 6): This point suggests that at a moderate temperature (55°F), the swimmer spends 6 hours at the pool. This is also consistent with the general trend that moderate temperatures allow for more hours at the pool.
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(90, 2): This point is specified as being outside of the cluster, which means it may be an outlier. At a high temperature (90°F), a swimmer is only spending 2 hours at the pool, which is likely contrary to what we might expect (more hours spent at the pool during hotter weather). It could be interpreted as an anomaly or a rarity, and thus it might not necessarily understate the overall trend but highlight an outlier.
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(95, 8): At a very high temperature (95°F), a swimmer spends 8 hours at the pool, which aligns with the expected behavior of increased swimming hours with increased temperatures.
If we need to choose a point that would lead to an understatement of the relationship (implying the trend of more hours at the pool) based on high temperatures, the most remarkable outlier affecting the expected results is point (90, 2). It shows that despite high temperatures, the swimmer spends hardly any time at the pool, potentially leading to an understated interpretation regarding the relationship between temperature and swimming hours.
Thus, the answer is: (90, 2)
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