To determine which option can use both intercepts to produce viable solutions, we should consider what each pair of variables represents and whether they can logically intersect in a way that makes sense in a real-world scenario.
-
(years of experience, starting salary): This relationship is commonly represented in a linear model. The y-intercept may represent the salary when experience is zero (e.g., entry-level jobs) and the x-intercept may not be particularly meaningful here.
-
(number of nominations, number of awards): A direct relationship might not yield both intercepts logically. For instance, if someone has zero awards, it could be because they've not received any nominations, so the x-intercept and y-intercept may not be meaningfully distinct.
-
(temperature, number of car rentals): This could logically have both intercepts. For instance, with an increase in temperature, the number of car rentals might increase, but there may also be a base level of rentals even at a lower temperature (e.g., due to business travelers), making both variables potentially viable in conjunction with their intercepts.
-
(age, weight): Generally, age and weight may show some relationship but do not clearly utilize intercepts to create viable or meaningful solutions.
Considering the analysis, the best option that could use both intercepts to produce viable solutions is (temperature, number of car rentals).