In “Boston’s Economic Diary,” we embark on a comprehensive journey through the economic heartbeat of Boston from 2013 to 2019. This detailed study utilizes a spectrum of data to paint a vivid picture of the city’s economic growth and challenges during this period. Key Highlights of the Analysis: Sectoral Interplay…
In our recent project, we utilized a neural network to predict job trends in Boston, focusing on the correlation between economic indicators like airport traffic and hotel rates and the number of jobs available. Key Aspects of the Neural Network Analysis: Model Composition: Our neural network, comprising multiple layers, was…
We’ve recently conducted a correlation analysis on Boston City’s economic data from 2013 to 2019, revealing some intriguing relationships between the labor market, tourism, and housing prices. Key highlights include a strong negative correlation between total jobs and housing prices, and an unexpected positive correlation between the unemployment rate and…
Below is an updated report for the Police Shooting incident happened in the USA as per The Washington Post.
The Correlation Matrix: A Window into Economic Dynamics Our correlation matrix included key indicators such as Logan Passengers, Hotel Occupancy Rate, Total Jobs, Unemployment Rate, and Median Housing Prices. This matrix serves as a roadmap, revealing how these variables move in tandem or opposition. Air Travel and Tourism: A Synchronized…
In the fast-paced world of economic analysis, understanding trends over time is crucial for making informed decisions. The Boston Redevelopment Authority’s dataset offers a goldmine of information, showcasing various economic indicators from 2013 to 2019. Our focus here is to dissect these trends through time series analysis, specifically examining Logan…
Introduction: Embarking on a journey into Boston’s economic landscape, we’ve delved into the city’s indicators from 2013 to 2019. Let’s discuss the insights uncovered and chart a course for actionable steps. Tourism and Hospitality Focus: Explore trends in Logan Airport’s passenger traffic and international flights. Analyze hotel occupancy rates and…
In “Beneath the Badge,” we delve deep into the critical and often contentious subject of police shooting incidents in the United States. This comprehensive study leverages detailed data analysis to uncover the underlying patterns and key insights into these incidents. Key Highlights of the Analysis: Demographic Trends: A closer look…
After confirming stationarity, I was ready to construct a model to forecast police shootings. I chose the SARIMAX model for its ability to handle both seasonal and non-seasonal elements. Here’s a snapshot of what I found: Model: SARIMAX(1, 1, 1)x(1, 1, 1, 12) AIC: 12537.839 BIC: 12568.175 The AIC and…
In my recent foray into the world of time series analysis, I took a deep dive into the patterns of police shootings across the United States. The first step in this journey was to determine if the data at hand was actually predictable – for this, I turned to the…
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}…
Today was a day of deep dives. I got my hands dirty with implementing the Random Forest regression model on our U.S. counties’ health metrics data. The objective was clear: to make sense of the complex relationships between various health indicators like diabetes rates, obesity levels, and physical inactivity. But…
The analysis begins with data cleaning and standardization, followed by k-means clustering to group counties based on their health metrics. The optimal number of clusters was determined to be four. Various visualizations were created to explore these clusters. Key Findings 1. Cluster-wise Box Plots Box plots were used to visualize…
After exploring the states with the highest and lowest rates of diabetes, obesity, and physical inactivity, I delved deeper to understand how states could be grouped based on these health metrics. Through clustering analysis, I found some intriguing patterns that might offer valuable insights for public health interventions. The Power…
I analyzed data from the Centers for Disease Control and Prevention (CDC) for the year 2018 to identify states that are most affected by these conditions. My Findings on Geographic Variations: Top 5 States with the Highest Rates of Diabetes: Through my analysis, I found that the states most affected…
This post aims to provide a statistical overview of that dataset, offering insights into the average rate of diabetes, its variability, and the range of percentages across counties. Key Statistical Findings: Count: A total of 3,142 counties were surveyed in 2018. Mean: The average rate of diabetes was approximately 8.72%…
In today’s analysis, I begins with an introduction to the analysis and focuses on exploring relationships between diabetes, obesity, and inactivity rates. The data is loaded from an Excel file, and necessary libraries are imported. Step 1: Uploading the Excel File The analysis starts by prompting the user to upload…
Hey, guys! So, let’s talk about something we all encounter but might not always understand—data and numbers! I know, I know, just hearing terms like “multi-linear regression” or “R-squared” might make you want to run for the hills. But trust me, they’re not as scary as they sound. In fact,…
Today, I took a close look at the 2018 health data from the CDC, focusing on rates of Diabetes, Obesity, and Inactivity across different U.S. counties. First, I got a sense of the average rates and how much they vary from place to place. Next, I looked at how “out…
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