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, t

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; and

• respondents that donate to charity;

• the perception of corruption in the government and businesses; and

• respondents amount of laughter and enjoyment on a day-to-day basis;

• along with those respondents’ answers regarding worry, sadness, and anger; and

• the 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; and

• delivery quality, measured by the responses to government effectiveness, regulatory quality,

and the effectiveness of law and order, and the effectiveness in controlling corruption?

2) Using different regression methods, quantile random forest and traditional random forest

modeling produces different results. Using these methods of analysis, what aspect of the

modeling generates different results with the World Happiness Report data for 2020 analysis?

Objective: Using a quantile regression random forest model and the traditional modeling via random forest,

answer the research questions. The discussion around the second research question will not be

written or addressed like directions from a book, because it will be addressed and explained in

terms of this data specifically.

Data:

• The World Happiness Report is published annually. This research will utilize the data from 2020.

• Helliwell, J. F., Layard, R., Sachs, J., & De Neve, J.-E. (Eds.). (2020). World happiness report 2020.

Sustainable Development Solutions Network. https://worldhappiness.report/ed/2020/

o Data dictionary and data

https://happiness-report.s3.amazonaws.com/2020/WHR20_Ch2_Statistical_Appendix.pdf

http://happiness-report.s3.amazonaws.com/2020/WHR20_DataForTable2.1.xls

o Read the URL into R as an xls. One option is with the library gdata, function read.xls().

• The countries that apply to this analysis are from south-eastern Asia and eastern Europe:

"Cambodia", "Indonesia", "Laos", "Malaysia", "Myanmar", "Philippines", "Singapore", "Thailand",

"Vietnam", "Belarus", "Bulgaria", "Czech Republic", "Hungary", "Poland", "Moldova", "Romania", "Russia",

"Slovakia", "Ukraine"

Requirements for this data analysis project:

• The focus will not include “using R”, “using RStudio”, or any other reference to programming.

• Make sure to submit all files necessary to make the program fully functioning.

• The program R script, the slides submitted and the slides presented shall match exactly. Do not change

the slides after submitting.

• All analyses require meaningful interpretations. Your programming should align with the findings.

• When completing the within group review, provide thorough feedback for every member of the group.