Samuel de la Rosa, Juan Pablo Castaño, Juan Pablo González

In this lab, I explored how sample size and confidence level affect the width and coverage of confidence intervals using simulated data on U.S. adults’ beliefs about climate change. Through the infer package in R, I learned how to construct bootstrap confidence intervals, interpret their meaning, and visualize how interval coverage changes across 90%, 95%, and 99% confidence levels. This report demonstrates how higher confidence leads to wider intervals and how sampling variability impacts real-world inference.

La siguiente es la **Guía 2: Producción de Acero – Comparando Grupos con ANOVA en GitHub Codespaces con Copilot**, siguiendo la misma lógica de trabajo que en la Guía 1, pero aplicada al dataset `acero.csv` y al documento de apoyo.

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