Challenge: Analyze Relationship Between Income and Health Outcomes
Understanding the relationship between income and health outcomes is crucial for effective policy evaluation. When a strong correlation exists between average income and measures such as life expectancy, it suggests that economic factors may play a significant role in public health. Policymakers can use these insights to target interventions, allocate resources more equitably, and design programs addressing socioeconomic disparities. However, it is important to remember that correlation does not imply causation—other factors may contribute to observed trends, and further analysis is necessary before drawing definitive conclusions.
12income = [35000, 42000, 39000, 47000, 41000, 36000, 38000] life_expectancy = [75.2, 78.1, 76.5, 80.3, 77.8, 75.9, 76.7]
Interpreting correlation coefficients in a policy context involves understanding both the direction and strength of the relationship. The Pearson correlation coefficient ranges from -1 to 1. A value close to 1 indicates a strong positive relationship (as income increases, life expectancy tends to increase), while a value close to -1 shows a strong negative relationship (as income increases, life expectancy tends to decrease). Values near 0 suggest little or no linear relationship. In practical terms, a strong positive correlation between income and health outcomes could support policies aimed at reducing income inequality to improve public health.
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Write a function that calculates the Pearson correlation coefficient between two lists: income and life_expectancy. The function should return both the correlation value and a brief interpretation of its strength and direction.
- Compute the Pearson correlation coefficient between
incomeandlife_expectancy. - Determine the strength of the correlation as "strong," "moderate," "weak," or "no" based on the absolute value of the coefficient.
- Determine the direction as "positive" or "negative" based on the sign of the coefficient.
- Return the correlation value and a string interpretation (e.g., "strong positive correlation").
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Understanding the relationship between income and health outcomes is crucial for effective policy evaluation. When a strong correlation exists between average income and measures such as life expectancy, it suggests that economic factors may play a significant role in public health. Policymakers can use these insights to target interventions, allocate resources more equitably, and design programs addressing socioeconomic disparities. However, it is important to remember that correlation does not imply causation—other factors may contribute to observed trends, and further analysis is necessary before drawing definitive conclusions.
12income = [35000, 42000, 39000, 47000, 41000, 36000, 38000] life_expectancy = [75.2, 78.1, 76.5, 80.3, 77.8, 75.9, 76.7]
Interpreting correlation coefficients in a policy context involves understanding both the direction and strength of the relationship. The Pearson correlation coefficient ranges from -1 to 1. A value close to 1 indicates a strong positive relationship (as income increases, life expectancy tends to increase), while a value close to -1 shows a strong negative relationship (as income increases, life expectancy tends to decrease). Values near 0 suggest little or no linear relationship. In practical terms, a strong positive correlation between income and health outcomes could support policies aimed at reducing income inequality to improve public health.
Swipe to start coding
Write a function that calculates the Pearson correlation coefficient between two lists: income and life_expectancy. The function should return both the correlation value and a brief interpretation of its strength and direction.
- Compute the Pearson correlation coefficient between
incomeandlife_expectancy. - Determine the strength of the correlation as "strong," "moderate," "weak," or "no" based on the absolute value of the coefficient.
- Determine the direction as "positive" or "negative" based on the sign of the coefficient.
- Return the correlation value and a string interpretation (e.g., "strong positive correlation").
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