Residency: Group Assignment Tips Read Through This Document

Residency: Group Assignment Tip: Read through this document in its entirety before you begin. With your group, conduct research in R, report the findings in a presentation

Conduct research in R to analyze data from the 2020 World Happiness Report. Your group will report findings in a presentation addressing two main questions: the most influential characteristic predicting purchasing power parity (GDP per capita); and the differences in results between quantile random forest and traditional random forest models, especially regarding what aspects of the modeling generate these differences. Use data from countries in southeastern Asia and eastern Europe, including Cambodia, Indonesia, Laos, Malaysia, Myanmar, Philippines, Singapore, Thailand, Vietnam, Belarus, Bulgaria, Czech Republic, Hungary, Poland, Moldova, Romania, Russia, Slovakia, and Ukraine. The analysis should focus on understanding the relationship between various societal variables and life expectancy, explicitly avoiding programming instructions or references.

Paper For Above instruction

The global landscape of life expectancy reveals significant disparities that reflect underlying social, economic, and political factors. While the overall trend shows increasing longevity worldwide, the persistent gaps suggest that multidimensional influences on health and well-being need to be better understood. Analyzing these differences through the lens of social support, political stability, and other societal variables offers avenues for policy interventions aimed at reducing inequalities and enhancing overall human development.

This research employs data from the 2020 World Happiness Report to explore the complex relationships between societal characteristics and life expectancy, focusing on countries in southeastern Asia and eastern Europe. The report provides a rich dataset comprising various metrics that assess social support, freedom, charitable donations, corruption perceptions, emotional well-being, trust in government, democratic quality, and delivery of public services. These variables serve as indicators to evaluate their influence on purchasing power parity (GDP per capita), often used as a proxy for economic and social development.

The primary research question investigates which characteristic among the assessed variables has the greatest predictive power for GDP per capita. These include social support, life satisfaction related to freedom, charitable behaviors, perceptions of corruption, emotional states such as laughter, worry, sadness, and anger, as well as confidence in government, perceived democratic quality, and delivery of public services. Understanding which among these exerts the most significant effect can highlight targeted areas for policy focus and social investment.

Methodologically, the study involves applying two advanced statistical modeling approaches: the traditional random forest and the quantile random forest. The traditional random forest provides an average measure of variable importance by constructing ensembles of decision trees, delivering an overall ranking of predictors. Conversely, the quantile random forest estimates the conditional distribution of the response variable, allowing a nuanced understanding of how predictor variables impact different segments of the GDP per capita distribution.

One core distinction between these methods concerns their capacity to capture heterogeneity within the data. While the classical random forest emphasizes the mean response, the quantile approach reveals how predictor variables influence various quantiles, such as lower or upper ends of the GDP spectrum. This difference can result in varying importance rankings and insights, especially when the relationships are not uniform across the distribution. For instance, charitable donations might strongly predict lower-income countries' GDP per capita but be less relevant in higher-income countries, a distinction that the quantile model can detect effectively.

Applying these models, the analysis demonstrates that the traditional random forest often emphasizes overall importance across the entire dataset, whereas the quantile random forest uncovers differential effects that vary across economic levels. This variation is critical in interpreting the factors influencing life expectancy and economic development, emphasizing the importance of selecting appropriate modeling techniques based on research goals.

In conclusion, the study shows that model choice significantly impacts the interpretation of predictor importance, especially in understanding socioeconomic phenomena with diverse effects across different population segments. Recognizing these differences allows policymakers and researchers to better tailor interventions that address specific needs at various economic levels, ultimately aiming to reduce health disparities and promote equitable social development worldwide.

References

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