This code will help provide a colorful correlation plot displaying the Pearson correlation coefficient, a 95% confidence interval (CI) around the correlation coefficient, the p-value, and a indicator of effect size magnitude following Cohen's (1988) suggested small, medium, and large effect size thresholds.

I don't know what I did but it finally works.

Analysis and results for Summer 25 comebacks/sports psychology study

I’m excited to share the results of my final data project for the Montgomery County Alcohol Beverage Services (ABS) team as part of my data science course! Over the past few months, I’ve been working on a real-world inventory optimization challenge, analyzing sales data across three different retail store types (high-, mid-, and low-volume). The goal: improve the algorithm for calculating Minimum Shelf Stock and Reorder Quantities using a smarter, data-driven approach. Project Highlights: - Cleaned and explored 53 weeks of 2024 sales data using R - Designed and implemented two algorithms (baseline & improved model) - Factored in demand trends, delivery schedules, and sales variability - Created visualizations to evaluate performance across store types

Instrucciones Objetivo: Realizar un documento RMD para visualizar reducción de componentes y conglomerado MDS Recursos: Para el presente trabajo necesitamos descargar la base vinos que reside en el siguiente enlace Siga las indicaciones el siguiente archivo RMD, en el encontrará los detalles de los que se espera recibir de parte del estudiante . Cuando complete el documento renderize el archivo. Proceda a publicar el documento en el repositorio de RPUBS