This assignment explores **housing price prediction and model interpretability** using the **Ames Housing Dataset** in R. The objective is to build, evaluate, and interpret a multiple linear regression model that predicts residential property sale prices based on structural, quality, and location-related variables. Through a series of analytical questions, the project covers the full regression workflow — from **data loading and preprocessing**, to **model building**, **assumption testing**, **validation**, and **prediction uncertainty**. The analysis applies fundamental statistical theory (BLUE: Best Linear Unbiased Estimator) while integrating practical, real-world reasoning to connect quantitative results with housing market behavior. Each section mirrors an applied data analytics process: - **A–B:** Prepare and fit a predictive model. - **C–D:** Identify key factors and assess statistical significance. - **E–G:** Evaluate model fit, test predictive accuracy, and interpret confidence and prediction intervals. The ultimate goal is to understand **how and why regression works**, not just how to run it — developing intuition for interpreting coefficients, evaluating significance, diagnosing assumptions, and communicating results meaningfully. This assignment demonstrates how data analytics and AI-assisted exploration can enhance evidence-based decision-making in **real estate valuation** and **economic modeling**.

En esta publicación se presenta el desarrollo y los resultados obtenidos al solucionar el caso solicitado por el docente.

Here you will find instructions for building and publishing your own RShiny app from open source iNaturalist data

Se realiza un análisis espacial en una zona determinada donde están sembrados morichales, y se quiere si se ubican aleatoriamente, hay grupos (cluster) o son regulares.

Homework