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Find an example of data presented in some print medium–newspaper, magazine, journal, etc.–and focus on the measures of central tendency and measures of variability.

Sure! Let’s consider a hypothetical example from a newspaper article discussing the average income and income inequality in a certain region.

Data presented: The article provides information on the incomes of individuals in a particular city district:

1. John earns $40,000 per year.
2. Mary earns $50,000 per year.
3. David earns $60,000 per year.
4. Sarah earns $70,000 per year.
5. Mark earns $200,000 per year.

Measures of Central Tendency:

1. Mean (Average): To calculate the mean income, we add up all the incomes and divide by the number of individuals. Mean = (40,000 + 50,000 + 60,000 + 70,000 + 200,000) / 5 = $88,000. So, the average income in this district is $88,000 per year.

Measures of Variability:

1. Range: The range is the difference between the highest and lowest incomes. Range = Highest Income – Lowest Income = $200,000 – $40,000 = $160,000. So, the range of incomes in this district is $160,000.
2. Standard Deviation: Standard deviation measures the dispersion or spread of values around the mean. It gives an idea of how much the incomes deviate from the average income. Calculating standard deviation requires more detailed data, including all individual incomes, which are not provided in this example. However, if we had the complete dataset, we could calculate it.

Analyzing this hypothetical data:

• The mean income gives us a central value around which the incomes are distributed.
• The range tells us the difference between the highest and lowest incomes, indicating the spread of incomes in the district.

However, keep in mind that this is a simplified example. In a real-world scenario, more data points and a broader range of statistical measures would be needed for a comprehensive analysis of income distribution and inequality.

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