460.The Dark Side of Data: Eliminating Outliers in Crime Datasets with SAS
Bloodlines & Broken Data: Mastering Outlier Detection in SAS with a Global Crime Dataset 1. Introduction Imagine you’re working on a global crime analytics project for an international intelligence agency. The goal is simple identify patterns in high-profile murder cases across countries. But when you open the dataset, chaos greets you. Victim ages are negative. Dates like 2023-02-30 exist. Countries appear as “usa”, “USA”, and “Usa”. Some murder counts are absurdly high like 10,000 cases in a small town. Duplicate records silently distort trends. This isn’t just messy it’s dangerous. Bad data doesn’t just slow analysis; it destroys decision-making . In clinical trials, it could mislead drug safety conclusions. In business, it could result in million-dollar mistakes. This is where SAS and R become powerful allies . They don’t just process data they enforce discipline, structure, and validation. And today, we’ll go deeper into one critical aspect: Detecting Outliers i...