To determine which options could use both intercepts to produce viable solutions, let's analyze each pair:
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(years of experience, starting salary): This correlation can be represented in a way where both the x-intercept (years of experience) and y-intercept (starting salary) can indicate meaningful values. For example, a person with zero years of experience may potentially have a starting salary of zero, making this a viable option.
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(temperature, number of car rentals): While we might assume that there’s a relationship (e.g., more rentals in warm weather), the x-intercept (temperature) doesn't have a universal viable meaning since temperature can go potentially below zero (e.g., if zero rentals correspond to extremely low temperatures). It may not have a meaningful interpretation for both axes.
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(age, weight): This relationship doesn't have clear intercepts leading to viable solutions, as age can be zero but weight cannot be negative, making the relationship less meaningful when interpreting the intercepts.
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(number of nominations, number of awards): This does have viable intercepts; if there are zero nominations, it would typically imply zero awards. This relationship would be meaningful, as you can have a scenario where no nominations lead to no awards.
From this analysis, the pairs that allow for both the x and y intercepts to produce viable solutions are:
- (years of experience, starting salary)
- (number of nominations, number of awards)
However, if you have to select just one option from your list, the best fit based on common reasoning would be (years of experience, starting salary) as it generally demonstrates a consistent, viable relationship.
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