Vizly: Analyzing Correlations Between Life Expectancy, Child Mortality, and Gross Income

Vizly: AI-powered data analysis

Analyzing Correlations Between Life Expectancy, Child Mortality, and Gross Income

Correlation between life expectancy and health indicators

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Correlation Summary

The correlation analysis between life expectancy (LEB) and health indicators (CM and GI) reveals:

LEB and CM (Child Mortality): High negative correlation (-0.994767), indicating that as child mortality decreases, life expectancy increases.
LEB and GI (Gross Income): Moderate negative correlation (-0.606101), suggesting that as gross income decreases, life expectancy tends to decrease, but the relationship is not as strong as with child mortality.
CM and GI: Positive correlation (0.627874), indicating that as gross income decreases, child mortality tends to increase.

These results highlight the significant impact of child mortality on life expectancy and a notable but less pronounced effect of gross income on life expectancy.

Compare life expectancy across different regions

To compare life expectancy across different regions, we'll group the data by region and calculate the average life expectancy for each. This will give us a clear picture of how life expectancy varies geographically. Let's start by examining the data for regional information.

Group data by region

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Explore other factors impacting life expectancy

To explore other factors impacting life expectancy, we'll look into variables such as healthcare quality, education levels, and economic factors beyond gross income. This involves correlating these factors with life expectancy to identify significant relationships. However, we need data that includes these variables to proceed.

Investigate correlation with healthcare quality

To investigate the correlation between life expectancy and healthcare quality, we'll calculate the correlation coefficient between life expectancy and a healthcare quality index, if available in the data. This step will help us understand the impact of healthcare quality on life expectancy. Let's check if the dataset includes a healthcare quality index.

Check for healthcare quality index in the dataset

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Analyze economic factors affecting life expectancy

To analyze economic factors affecting life expectancy, we'll focus on variables like GDP per capita, unemployment rate, and income inequality. These factors can provide insights into the economic conditions that influence life expectancy. However, we need to ensure the dataset includes these variables to proceed with the analysis.

Explore correlation with income inequality

To explore the correlation between life expectancy and income inequality, we'll calculate the correlation coefficient between life expectancy and an income inequality index, such as the Gini coefficient, if it's available in the dataset. This will help us understand how income distribution within a population impacts life expectancy. Let's check if the dataset includes an income inequality index.

Check for Gini coefficient in the dataset

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Summary of Findings

Correlation between Life Expectancy and Health Indicators:
- A strong negative correlation between life expectancy and child mortality (CM) was identified, indicating that lower child mortality is associated with higher life expectancy.
- A moderate negative correlation between life expectancy and gross income (GI) was observed, suggesting that lower gross income may lead to a lower life expectancy, though the relationship is less pronounced compared to child mortality.
Comparison of Life Expectancy Across Regions:
- The dataset does not contain regional information, preventing analysis of life expectancy variations across different regions.
Investigation of Other Factors Impacting Life Expectancy:
- The dataset lacks variables such as healthcare quality index and Gini coefficient, which are crucial for exploring the impact of healthcare quality and income inequality on life expectancy.
Economic Factors Affecting Life Expectancy:
- Without specific economic variables like GDP per capita, unemployment rate, and income inequality indices in the dataset, analyzing the economic factors affecting life expectancy is not feasible.

Visualize this for me

To visualize the findings, we need to create plots that reflect the correlations and available data. Since our dataset lacks certain variables for a comprehensive analysis, we'll focus on visualizing the correlation between life expectancy (LEB), child mortality (CM), and gross income (GI). We'll create a scatter plot for each pair of variables to illustrate their relationship.

Let's start by visualizing the correlation between life expectancy and child mortality, followed by life expectancy and gross income.

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Visualization Summary

Life Expectancy vs. Child Mortality:
- The scatter plot illustrates a clear negative correlation between life expectancy and child mortality. This confirms our earlier analysis indicating that lower child mortality is associated with higher life expectancy.
Life Expectancy vs. Gross Income:
- The scatter plot shows a relationship between life expectancy and gross income, albeit less pronounced than with child mortality. It suggests that higher gross income may be associated with higher life expectancy, aligning with the moderate negative correlation identified earlier.