Question
Which of the following options could use both intercepts to produce viable solutions? (1 point)
Responses
(number of nominations, number of awards)
(number of nominations, number of awards)
(temperature, number of car rentals)
(temperature, number of car rentals)
(age, weight)
(age, weight)
(years of experience, starting salary)
Responses
(number of nominations, number of awards)
(number of nominations, number of awards)
(temperature, number of car rentals)
(temperature, number of car rentals)
(age, weight)
(age, weight)
(years of experience, starting salary)
Answers
GPT-4o mini
To determine which options could use both intercepts to produce viable solutions, we need to consider whether the variables involved can logically have intercepts that make sense in the context.
1. **(number of nominations, number of awards)** - This can have intercepts: a scenario with 0 nominations would correspond to 0 awards, and this is reasonable.
2. **(temperature, number of car rentals)** - This could also have logical intercepts. For example, at very low temperatures, the number of car rentals could drop to zero.
3. **(age, weight)** - This one is tricky. While there are intercepts (like a newborn's weight), the relationship between age and weight is not strictly linear and may not make much sense for intercepts beyond the newborn/infant stage.
4. **(years of experience, starting salary)** - This option makes sense as well. At zero years of experience, there is typically an expected starting salary, defining a clear intercept.
Given these analyses, the options that can produce viable solutions using both intercepts are:
- (number of nominations, number of awards)
- (temperature, number of car rentals)
- (years of experience, starting salary)
Therefore, any one of these three options could be correct. However, if you need to select only one:
- The most straightforward and commonly used in a linear relationship context would be **(years of experience, starting salary)**, as it clearly reflects a common scenario in job markets.
So, you could select **(years of experience, starting salary)** for a clean, primary example of using both intercepts.
1. **(number of nominations, number of awards)** - This can have intercepts: a scenario with 0 nominations would correspond to 0 awards, and this is reasonable.
2. **(temperature, number of car rentals)** - This could also have logical intercepts. For example, at very low temperatures, the number of car rentals could drop to zero.
3. **(age, weight)** - This one is tricky. While there are intercepts (like a newborn's weight), the relationship between age and weight is not strictly linear and may not make much sense for intercepts beyond the newborn/infant stage.
4. **(years of experience, starting salary)** - This option makes sense as well. At zero years of experience, there is typically an expected starting salary, defining a clear intercept.
Given these analyses, the options that can produce viable solutions using both intercepts are:
- (number of nominations, number of awards)
- (temperature, number of car rentals)
- (years of experience, starting salary)
Therefore, any one of these three options could be correct. However, if you need to select only one:
- The most straightforward and commonly used in a linear relationship context would be **(years of experience, starting salary)**, as it clearly reflects a common scenario in job markets.
So, you could select **(years of experience, starting salary)** for a clean, primary example of using both intercepts.