To determine which pair of variables can use both intercepts to produce viable solutions, we should consider the nature of each pair of variables and how they are typically represented on Cartesian coordinates.
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A. (Temperature, number of car rentals): This pair could potentially produce viable business solutions, as temperature might affect the number of car rentals. However, we usually measure temperature in degrees (which can be negative) and number of car rentals (which cannot be negative), limiting how intercepts can be used.
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B. (Age, weight): Both of these variables typically yield a positive relationship, with age being a non-negative integer. Therefore, this combination would not effectively use both intercepts to produce viable solutions, as negative values in age or weight wouldn’t make practical sense.
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C. (Years of experience, starting salary): This is a common relationship in which we expect the starting salary to increase as years of experience increase. Both years and salary values are typically non-negative, making effective interpretation of intercepts less viable for this relationship.
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D. (Number of nominations, number of awards): Both of these variables are non-negative integers. The intercepts in this context could be interpreted effectively, as both nominations and awards could start at zero, which allows for an understanding of how many nominations might lead to how many awards, while also making both intercepts practically meaningful.
Given the options, D. (Number of nominations, number of awards) would be the most viable pair of variables that could use both intercepts to produce meaningful solutions.
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