This analysis examines institutional and individual-level predictors of preventable custodial death across four Texas county jail systems Bexar, Dallas, Harris, and Travis using data from 390 deaths recorded between 2015 and 2025. Data were sourced from the Texas Office of Attorney General custodial death reporting records and analyzed using binary logistic regression in R. Raw preventable death rates reveal striking county-level disparities: Travis County (27.9%) and Bexar County (27.7%) each classify more than one in four custodial deaths as preventable, while Dallas County (7.8%) and Harris County (8.6%) maintain substantially lower rates. Together, Bexar and Travis counties account for 70% of all preventable deaths in the sample despite representing 42% of total deaths. Two logistic regression models were estimated. The primary model coded Bexar County as a binary indicator variable and found that incarceration in Bexar County was associated with 3.3 times greater odds of preventable death (OR = 3.303, 95% CI [1.669, 6.676], p = .001) after controlling for age, sex, race, mental health status, suicidal ideation, and housing type. A supplemental all-county comparison model, with Bexar as the reference group, found that Dallas County inmates had 79% lower odds of preventable death (OR = 0.208, p = .005) and Harris County inmates had 70% lower odds (OR = 0.304, p = .004) compared to Bexar. Travis County did not differ significantly from Bexar (OR = 0.445, p = .119), confirming both counties share comparably elevated preventable death risk. At the individual level, suicidal ideation was the strongest predictor of preventable death (OR = 5.474, p = .006), followed by single-cell housing (OR = 2.580, p = .004) and younger age (OR = 0.928 per year, p < .001). Mental health status alone was not a significant predictor in the full model, suggesting that general documentation of mental health problems is insufficient without targeted institutional response protocols for acute crisis indicators. These findings provide empirical support for the argument that institutional practices not individual characteristics are the primary drivers of variation in preventable custodial death rates across Texas county jails. The existence of counties achieving preventable death rates below 9% demonstrates that current conditions in higher-rate counties represent institutional failures, not inevitable outcomes. This analysis was conducted as part of a Master of Public Administration capstone project at the University of Texas at San Antonio. The full paper examines policy implications and recommendations for statewide reform of Texas county jail death prevention standards.

Implementation of Principal Component Analysis (PCA) and Exploratory Factor Analysis (FA) on Taiwan Air Quality Index data (2016–2024). This study reduces 17 correlated environmental variables into five principal components/factors explaining approximately 74% of total variance, revealing distinct pollution dimensions including particulate matter, combustion-related gases, ozone dynamics, sulfur emissions and spatial–geographical variation.

Câu 1 (5 điểm) Sử dụng tập iris, dự đoán Petal.Length từ Sepal.Length và Sepal.Width. Kiểm tra độ phù hợp của mô hình bằng hệ số R bình phương hiệu chỉnh. Câu 2 (5 điểm) Sử dụng tập Heart, chạy hồi quy logistic dự đoán bệnh tim. Vẽ ROC curve.

In this data dive, I conducted an ANOVA test to compared FG% and position and a linear regression model to assess FG% and Average Shot Distance.