As of March 2, 2026

This is the Week 1 project for DATS201

Este proyecto tiene como objetivo actuar como un Analista Senior de Datos para evaluar la evolución de un sector económico específico en Colombia (2012-2024). Utilizaremos técnicas avanzadas de series de tiempo para transformar datos crudos en decisiones estratégicas e insights de negocio.

AQUI PTENEMOS LA TABBLA EN LA CUAL IDENTIFICAMOS POR MEDIO DE LA CANTIDAD DE PERSONCAS CON LAS QUE CUENTA LA EMPRESA SI PEQUEÑA MEDIANA O GRANDE

Tugas Praktikum 3 ini berfokus pada penerapan simulasi variabel random untuk memodelkan fenomena dunia nyata menggunakan perangkat lunak R. Implementasi ini mencakup dua kategori utama, yakni distribusi diskrit untuk data berupa cacahan angka bulat dan distribusi kontinu untuk data hasil pengukuran yang bersifat desimal. Melalui pembangkitan angka acak, karakteristik statistik seperti rata-rata (mean) dan probabilitas dianalisis untuk memvalidasi ketepatan model simulasi terhadap parameter teoretis yang telah ditentukan.

This codebook documents the complete R analysis pipeline used in a Master of Public Administration capstone study examining predictors of preventable custodial death across four Texas county jails Bexar, Dallas, Harris, and Travis from 2015 to 2025 (N = 390). All data were sourced from the Texas Office of Attorney General custodial death reporting records. The codebook is organized into nine sections covering the full analytical workflow: library setup, data loading and variable preparation, summary dataset construction, descriptive statistics, bivariate testing, correlation analysis, logistic regression modeling, data visualization, and a key results reference. All code is fully annotated and reproducible. Four logistic regression models are documented, including a demographics-only baseline model, a model adding mental health and custody time variables, a primary binary model coding Bexar County as an indicator variable, and a supplemental all-county comparison model with Bexar as the reference group. Significant predictors of preventable death identified in the primary model include Bexar County incarceration (OR = 3.303, p = .001), suicidal ideation (OR = 5.474, p = .006), single-cell housing (OR = 2.580, p = .004), and younger age (OR = 0.928 per year, p < .001). Six figures are included: preventable death rates by county, odds ratio forest plots for both primary and all-county models, and supporting graphs examining suicidal ideation, age distribution, and housing type by preventable death status. 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 institutional drivers of preventable custodial death and develops policy recommendations for statewide reform of Texas county jail death prevention standards