Happiness Scores Analysis Report

DOI

10.2991/978-2-494069-31-2_340How to use a DOI?

Keywords

Happiness scores; Cluster analysis; Predictive models; COVID-19

Abstract

Happiness scores are often used to inform policy decisions. This report is dedicated to analyzing happiness scores to support better government decision-making. In the exploratory data stage, statistical analysis, comparative distributions, regression analysis, and correlation analysis are carried out using Excel and Stata. Predictive models of regression and neural networks were built after cluster analysis using SAS Enterprise Guide and SAS Enterprise Miner Workstation. Average Squared Error (ASE) was a used to select the better regression prediction model. The impact of the COVID-19 and the public policies after the pandemic are briefly discussed.

Copyright

© 2022 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Lenong Xu
PY  - 2022
DA  - 2022/12/29
TI  - Happiness Scores Analysis Report
BT  - Proceedings of the 2022 6th International Seminar on Education, Management and Social Sciences (ISEMSS 2022)
PB  - Atlantis Press
SP  - 2892
EP  - 2901
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-494069-31-2_340
DO  - 10.2991/978-2-494069-31-2_340
ID  - Xu2022
ER  -