Grant number: | 16/11337-4 |
Support Opportunities: | Regular Research Grants |
Start date: | March 01, 2017 |
End date: | February 28, 2019 |
Field of knowledge: | Interdisciplinary Subjects |
Principal Investigator: | Salvador Boccaletti Ramos |
Grantee: | Salvador Boccaletti Ramos |
Host Institution: | Pró-Reitoria Adjunta de Pesquisa e Pós-Graduação. Universidade de Franca (UNIFRAN). Franca , SP, Brazil |
Associated researchers: | Antonio Sergio Ferraudo ; Mirelle Andréa de Carvalho Picinato ; Monica de Andrade |
Abstract
The Sustainable Development Goals (SDG) document lists goals and targets that should be dealt with to solve sustainable developmental global issues. One of these goals asks for more reliable indicators. An efficient way to meet this goal is by the means of statistical analysis that estimates association between indicators. Unfortunately, the vast majority of researches had studies low number of indicators at a time within only one or two areas in the sustainable development context. Furthermore, they estimate simple statistics like linear correlations or models that lack a reliable measurement of accuracy. The aim of this research proposal will be to verify associations and build prediction models for human development indicators and indexes from environmental indicators that deal with a large number of variables simultaneously. Public use datasets will be used. Multivariate analysis techniques will allow to explore the data structure and to verify similarities between indicators. Variable selection methods will allow to find the variables that best explain the variability of an indicator of interest. Methods of cross-validation will allow to build statistical models with better predictive ability. It is expected to empower managers and citizens with knowledge that motivates to act in a sustainable way and statistical models able to predict the rate of change of human development from environmental changes with high accuracy. (AU)
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