This section summarizes the PCA conducted on ten health-related indicators from 33 districts/cities in North Sumatra. After confirming data suitability through KMO and Bartlett’s tests, PCA was performed on the standardized dataset. Three principal components were retained, explaining 82.74% of the total variance. The first component represents general health indicators, the second reflects specific regional differences, and the third captures additional unique variation. Visualizations such as the scree plot, correlation circle, and contribution plots help identify the variables and regions most influential in shaping each component. Overall, PCA effectively reduces the dataset and highlights the key patterns underlying health factors related to poverty.

This report applies exploratory factor analysis (EFA) to datasets containing poverty-related indicators classified into health quality and economic quality dimensions across 33 regencies/cities in North Sumatra (2018). The analysis includes KMO test, Bartlett’s test, parallel analysis, varimax rotation, factor extraction, and interpretation of dominant indicators forming each latent factor.