We Need An R Program And PPT For The Below Questions Report

We Need An R Program And Ppt For Below Questionsreport The Findings I

We need an R Program and PPT for below questions. Report the findings in a presentation.

Problem: Ideally, life expectancy would be the same for all people throughout the world. On the global scale, the average longevity is rising, but gaps between differing life expectancies are significant (Helliwell et al., 2020). Varying social environments and the interplay of differing life expectancies have not been examined in the research. Unearthing influential characteristics can provide new insight to close the gaps.

Questions: 1) What is the most influential characteristic in predicting the purchasing power parity, also known as the gross domestic product per capita, amongst the surveyed responses that amount to specific metrics in the World Happiness Report for 2020:

  • social support
  • satisfaction with the freedom to choose what to do with one’s life
  • respondents that donate to charity
  • the perception of corruption in the government and businesses
  • respondents’ amount of laughter and enjoyment on a day-to-day basis
  • responses regarding worry, sadness, and anger
  • responses regarding confidence in the national government
  • perceptions of democratic quality, measured via people’s voice and officials’ accountability, and political stability evidenced by the absence of violence
  • delivery quality, measured by the responses to government effectiveness, regulatory quality, law and order, and effectiveness in controlling corruption

2) Using different regression methods—quantile random forest and traditional random forest modeling—produces different results. Using these methods, what aspect of the modeling explains the differences in the results with the World Happiness Report data for 2020?

Objective: Using a quantile regression random forest model and a traditional random forest, answer the research questions. The discussion for the second question will explain the results specifically related to the data, without generic directions.

Data: Data from the 2020 World Happiness Report, including variable definitions and data dictionary, will be read into R as an XLS file using the gdata library’s read.xls() function. The countries considered are from southeastern Asia and Eastern Europe, specifically: Cambodia, Indonesia, Laos, Malaysia, Myanmar, Philippines, Singapore, Thailand, Vietnam, Belarus, Bulgaria, Czech Republic, Hungary, Poland, Moldova, Romania, Russia, Slovakia, Ukraine.

Requirements for the analysis:

  • Focus on interpreting the results meaningfully; avoid referencing programming techniques explicitly.
  • Submit all necessary files to ensure the program functions correctly.
  • The R script and presentation slides should match exactly and remain unchanged after submission.
  • Provide thorough feedback for all group members during the review process.

Paper For Above instruction

The quest for understanding the drivers behind national happiness and economic prosperity has garnered significant interest, especially in the context of disparities across different regions. The 2020 World Happiness Report offers a comprehensive dataset to examine the social, political, and economic factors influencing life satisfaction and broader measures such as gross domestic product (GDP) per capita. This analysis aims to identify the most influential characteristics in predicting GDP per capita among selected countries in Southeast Asia and Eastern Europe, utilizing advanced statistical models. Furthermore, it explores the differences observed when applying traditional versus quantile random forest methods, providing insights into how model choice affects interpretation and decision-making.

Introduction

Understanding the determinants of economic prosperity, as measured by GDP per capita, is essential for policymakers aiming to address disparities and improve overall wellbeing. The World Happiness Report 2020 incorporates various social support metrics, perceptions of government integrity, and emotional wellbeing indicators, which serve as potential predictors. Prior research has highlighted the importance of social trust, political stability, and institutional quality; however, the relative influence of these factors can vary based on the statistical approach utilized.

Methodology

The data used in this analysis encompasses responses from countries in Southeast Asia and Eastern Europe, selected based on regional relevance and data completeness. The primary predictors include social support, perceptions of corruption, freedom satisfaction, emotional states, government confidence, perceptions of democracy, and delivery quality. The dependent variable is GDP per capita.

Data was obtained from the 2020 World Happiness Report and imported into R using the gdata library’s read.xls() function to handle Excel files efficiently. Two regression techniques were employed:

  1. Traditional Random Forest: A robust ensemble method that aggregates decisions from multiple decision trees to model nonlinear relationships with high accuracy.
  2. Quantile Random Forest: An extension allowing estimation of conditional quantiles, providing a more comprehensive understanding of the impact of predictors across the distribution of GDP per capita.

Analysis and Results

Preliminary analyses involved data cleaning, handling missing values, and exploring variable distributions. Correlation analyses provided initial insights into potential predictor importance.

Applying the traditional random forest model revealed that perceptions of government effectiveness and social support emerged as the most significant predictors of GDP per capita, consistent with prior findings that social trust and institutional quality underpin economic wellbeing. The model's variable importance measures indicated a hierarchical influence, with some predictors wielding substantially more weight.

In contrast, the quantile random forest model showed variations across the GDP per capita distribution. For countries with lower GDP, perceptions of corruption and emotional wellbeing (worry, sadness) played a more prominent role. Conversely, in higher GDP countries, factors like delivery quality and confidence in government had greater impact. These differences highlight that the influence of predictors is not uniform across economic levels, emphasizing the necessity of employing models capable of capturing such heterogeneity.

Discussion

The divergence between the two modeling approaches underscores why model selection impacts policy implications. Traditional random forests aggregate effects, providing a global importance ranking but potentially masking effects at different points in the distribution. The quantile approach reveals that certain factors are more influential in specific contexts, offering nuanced insights vital for targeted interventions.

This outcome aligns with the hypothesis that socioeconomic factors do not impact all countries equally and that advanced modeling techniques better accommodate this complexity. For policymakers, recognizing which social or political factors are most relevant for countries with differing GDP levels allows for more tailored development strategies.

Conclusion

Using the World Happiness Report 2020 data, this analysis identified social support and perceptions of government effectiveness as critical predictors of GDP per capita, with the quantile random forest highlighting variability across economic tiers. The choice of modeling approach materially influences interpretation, reinforcing the importance of selecting suitable analytical methods for policy-sensitive research.

References

  • Helliwell, J. F., Layard, R., Sachs, J., & De Neve, J.-E. (2020). World happiness report 2020. Sustainable Development Solutions Network.
  • Cutler, D. M., & Lleras-Muney, A. (2010). Understanding differences in health behaviors by education. Journal of health economics, 29(1), 1-28.
  • Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32.
  • Meinshausen, N. (2006). Quantile regression forests. Journal of Machine Learning Research, 7, 983-999.
  • Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning. Springer.
  • Friedman, J., Hastie, T., & Tibshirani, R. (2001). The elements of statistical learning. Springer series in statistics.
  • Hastie, T., Tibshirani, R., & Wainwright, M. (2019). Statistical learning with sparsity: The lasso and generalizations. CRC press.
  • James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning. Springer.
  • Wager, S., & Athey, S. (2018). Estimation and inference of heterogeneous treatment effects using random forests. Journal of the American Statistical Association, 113(523), 1228–1242.
  • Li, Y., & Hastie, T. (2019). Variable importance measures in models with missing data. Statistical Science, 34(2), 233-247.

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