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An economist uses a power-law probability distribution to model the wealth of two countries. The distribution for Country X is characterized by a shape parameter (alpha) of 1.5, while the distribution for Country Y is characterized by a shape parameter of 2.5. A lower value for this shape parameter indicates a 'heavier tail,' signifying a greater concentration of wealth among the highest earners. Which conclusion can be drawn from this information?
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- Dataset 1: The declared annual income of all households in the city. The data reveals that the top 1% of households hold 30% of the city's total income, and the top 10% hold 75% of the total income.
- Dataset 2: The measured heights of all adult residents in the city. The data shows that most people's heights are very close to the average height, with very few individuals being exceptionally tall or exceptionally short.
Which dataset would be best modeled by a probability distribution that is highly skewed and has a 'heavy tail,' meaning that a small number of high-magnitude events account for a large portion of the distribution?
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An economist uses a power-law probability distribution to model the wealth of two countries. The distribution for Country X is characterized by a shape parameter (alpha) of 1.5, while the distribution for Country Y is characterized by a shape parameter of 2.5. A lower value for this shape parameter indicates a 'heavier tail,' signifying a greater concentration of wealth among the highest earners. Which conclusion can be drawn from this information?
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