Journal article Open Access

Mapping the Statistical Significance of Factors Contributing to the World Happiness Report

Karan Bhowmick; Charuchith Ranjit

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      <creatorName>Karan Bhowmick</creatorName>
      <affiliation>Department of Information Technology, Vellore Institute of Technology, Vellore (Tamil Nadu), India., Department of Information Technology, Vellore Institute of Technology, Vellore (Tamil Nadu), India.</affiliation>
      <creatorName>Charuchith Ranjit</creatorName>
      <affiliation>Department of Computer Science with  Specialization in Bioinformatics, Vellore Institute of Technology, Vellore Tamil Nadu, India.</affiliation>
    <title>Mapping the Statistical Significance of Factors Contributing to the World Happiness Report</title>
    <subject>RMSE, MSE, VIF, Ladder Score, GDP, Cross  Validation, Overfitting, Underfitting.</subject>
    <subject subjectScheme="issn">2249-8958</subject>
    <subject subjectScheme="handle">100.1/ijeat.F29630810621</subject>
    <contributor contributorType="Sponsor">
      <contributorName>Blue Eyes Intelligence Engineering  and Sciences Publication (BEIESP)</contributorName>
    <date dateType="Issued">2021-08-30</date>
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    <relatedIdentifier relatedIdentifierType="ISSN" relationType="IsCitedBy" resourceTypeGeneral="JournalArticle">2249-8958</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.35940/ijeat.F2963.0810621</relatedIdentifier>
    <rights rightsURI="">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
    <description descriptionType="Abstract">&lt;p&gt;This paper aims to delineate findings of the statistical significance of the factors contributing to the happiness score. The happiness score also termed as ladder score, is a metric used by the United Nations Sustainable Development Solutions Network to metricize the happiness of the citizens in a country. To tackle this issue, we use regression and data visualization. We perform a survey on the factors affecting ladder score and how these factors can be used for predictive analytics. We use Linear Regression, Polynomial Regression, Lasso Regression with cross-validation, and Ridge Regression with cross-validation. Next, we use evaluation metrics like MSE, RMSE, Adjusted r-squared, and r-squared value for the evaluation of the factors on the predictive model. Then, we plot the countries mentioned in the report on a geographical scale based on their happiness index scores. Furthermore, we plot the statistical significance of these factors on a continental scale, to reveal insightful patterns over a larger geographical domain. We aim to bring to light the trends of the aforementioned factors and produce the significance of these results on a world map. The results will help elucidate the global patterns formed by these metrics. An additional application is an extrapolation of the results procured. To augment the metrics of the Word Happiness Report in a statistically comprehensive way. Furthermore, through this evaluation, the world happiness report can be revised to accommodate more inclusive factors and mitigate the redundancy of the factors.&lt;/p&gt;</description>
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