In an effort to better understand the dynamics of police shootings, we conducted a comprehensive statistical and clustering analysis. Here’s what we found: Kolmogorov-Smirnov Test on Age Distribution The Kolmogorov-Smirnov test was performed to assess whether the age distribution of individuals involved in police shooting incidents follows a normal distribution.…
Introduction Determining the right number of clusters is crucial in k-means clustering. The Elbow Method is a widely-used technique for this purpose. The Elbow Method This method involves calculating the inertia (sum of squared distances from each point to its cluster center) for various numbers of clusters and plotting these…
Our ongoing analysis of police shootings in the United States takes a nuanced turn as we delve into cluster sampling, focusing on 10 randomly selected states. This method allows us to examine state-specific trends and variables, thereby offering unique insights into these critical incidents. Methodology: Using cluster sampling, we selected…
In our ongoing exploration of police shootings in the U.S., today’s focus is on utilizing Monte Carlo simulations to better understand the average age of individuals involved in these fatal incidents. This statistical technique offers a robust method for understanding the distribution and variability of outcomes based on existing data.…
I focused today on understanding two specific aspects: the days of the week when most incidents occur and the usage of body cameras by law enforcement. Here’s what I found: Days of the Week Analysis: Key Finding: The incidents are fairly evenly spread across all days of the week. Notable…
Quick Stats Total Shootings: 8,770 Time Span: 2015-2023 States: 51 Cities: 3,374 Police Departments: 3,417 Yearly Trends: The graph below shows the number of fatal shootings has remained relatively stable from 2015 to 2023. Who’s Affected: Most individuals are males between 20-40 years old. Whites are the most represented racial…
I’m excited to share my latest project that dives deep into the public health metrics across U.S. counties. My study focuses on three key health indicators: Diabetes, Obesity, and Inactivity. The report aims to uncover the hidden patterns, relationships, and insights that can inform public health policies and interventions. The…
Introduction In the United States, public health has been a topic of concern, especially in the areas of Diabetes, Obesity, and Physical Inactivity. Understanding the risk factors at a granular level, such as the county level, can help policymakers take more effective action. This post explores a Decision Tree Classifier…
Key Findings: 1. State as a Significant Factor: For all three health metrics—Diabetes, Obesity, and Inactivity—the p-values are far below the commonly used significance level of 0.05. This indicates that the state a county belongs to is a statistically significant factor affecting these rates. Diabetes: p = 8.28 * 10^{-32}…
