PCA: a mathematical tool for the analysis of agricultural and livestock COREDEs in Rio Grande do Sul
DOI:
https://doi.org/10.5902/2179460X90532Keywords:
Principal component analysis, Spectral decomposition, Census of agriculture, COREDEsAbstract
This study aimed to reduce the original data matrix from the agricultural Regional Development Councils (COREDEs) of Rio Grande do Sul, utilizing information from the 2017 Agricultural Census. We commenced with a rigorous exposition on mathematical results underpinning Principal Components Analysis (PCA), culminating in an appliction of PCA that significantly reduced the initially collected data from 15 variables to only three components, which collectively explained approximately 87\% of the data variance. These insights have the potential to influence decision-making in agricultural policies and regional development strategies targeted at these COREDEs.
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