This analysis explores historical stock price trends using key technical indicators to assess market behavior. It includes: Daily Returns & Volatility: Understanding stock price fluctuations. Moving Averages (50-day & 200-day): Identifying market trends. Bollinger Bands: Detecting overbought and oversold conditions. Correlation Heatmap: Analyzing relationships between stock metrics. Key Insights: ✔ Moving averages help confirm trend direction. ✔ Bollinger Bands indicate potential price reversals. ✔ Correlation analysis aids in portfolio diversification. This structured approach provides valuable insights for traders and investors to enhance decision-making.

OHIO BRFSS Data Analysis

Implement an R function to generate a line graph depicting the trend of a time-series dataset, with separate lines for each group, utilizing ggplot2’s group aesthetic.

Write an R script to create a scatter plot, incorporating categorical analysis through color-coded data points representing different groups, using ggplot2.

Develop an R program to quickly explore a given dataset, including categorical analysis using the group by command, and visualize the findings using ggplot2 features.

Spring 2025 DATA 622