Linear transformations

This proposal explores how grocery pricing and store availability differ between low-income and high-income neighborhoods in New York City. Focusing on Brownsville (Brooklyn) and Lower Manhattan, the project investigates whether essential items like eggs and milk are more expensive or less accessible in areas with higher poverty rates. The analysis uses a placeholder dataset for now, with plans to incorporate real data from NYC Open Data, USDA Food Access Research Atlas, and the U.S. Census for the final project.

Menganalisis kondisi makroekonomi di masa mendatang pada tingkat regional, serta merumuskan strategi yang tepat dalam menghadapi dinamika ekonomi tersebut. Analisis dilakukan dengan menggunakan metode Vector Autoregression (VAR) berdasarkan data triwulan periode 2011–2025

This project demonstrates the transformation of the Insurance dataset from wide format to long (tidy) format using R's tidyverse package. The Insurance dataset contains health insurance information for 1,338 individuals, including demographic characteristics and healthcare charges. The primary objectives of this data transformation project are to: - Restructure the data from wide format (where multiple measurements exist as separate columns) to long format (where each measurement becomes its own row) - Apply tidy data principles to make the dataset more suitable for statistical analysis and visualization - Demonstrate best practices in data wrangling and preparation for data science workflows - Fulfill DATA 624 course requirements by showcasing proficiency in data transformation techniques essential for masters-level data analysis

Este es un currículo unificado de Estadística para Primaria