This dashboard presents a comprehensive analysis of climate change data using flexdashboard in R. It includes data cleaning, handling of missing values through group-wise imputation, and visualization of key climate indicators such as temperature, CO2 concentration, precipitation, and seasonal patterns. The dashboard follows best practices in data visualization to provide clear insights into trends, relationships, and environmental variability.

This data dive explores how overall NBA player performance has changed over time. Using a linear model and other time series visualizations, the analysis finds a small but significant upward trend, suggesting performance has slightly improved over the years. However, time alone does not explain most of the variation, indicating other factors also play an important role.

Presentation for 2026 CADSA Symposium

This project applies the Fama-French Three-Factor Model to a simulated Moroccan stock market dataset. The analysis constructs the market, size, and value factors and estimates their impact on portfolio returns using OLS regression. Diagnostic tests and robust (HAC) standard errors are used to ensure reliable inference. Results show that the three factors significantly explain returns, highlighting the relevance of multifactor models in emerging markets.