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Which of the following pairs of variables exhibit a positive correlation?

Rationale

Distance traveled and fuel used exhibit a positive correlation.

As the distance traveled increases, the amount of fuel consumed typically rises, demonstrating a direct relationship between these two variables. This correlation is commonly observed in vehicles, where longer trips generally require more fuel.

A (Temperature and heater usage) YOUR ANSWER

While it might seem that increased temperature would lead to decreased heater usage, the relationship is actually negative. As temperature rises, the need for heating decreases, indicating an inverse correlation rather than a positive one.

B (Distance traveled and fuel used) CORRECT

This pair shows a clear positive correlation. When a vehicle travels further distances, it consumes more fuel to maintain movement, aligning the two variables positively. This relationship is fundamental in understanding fuel efficiency and consumption in transportation.

C (Stock market goes down, people lose money) YOUR ANSWER

This statement describes a negative outcome rather than a correlation between two variables. While a decrease in the stock market often results in financial losses for investors, it does not express a positive correlation between two independent variables in the same way as the other options.

D (Age and life expectancy) YOUR ANSWER

Although it may seem that age and life expectancy are positively correlated, this relationship is not straightforward. As people age, their remaining life expectancy decreases, indicating a negative correlation rather than a positive one.

Conclusion

Understanding positive correlation is crucial in analyzing relationships between variables. In this context, distance traveled and fuel used clearly demonstrate a positive correlation, where an increase in one variable leads to an increase in the other. The other options either exhibit negative correlations or do not represent a correlation at all, highlighting the importance of correctly identifying relationships in data analysis.

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