This report analyzes the New York City Jobs dataset using Principal Component Analysis (PCA) and K-means clustering. The analysis focuses on numeric features such as the number of positions and salary ranges to identify patterns in job postings. First, the data is cleaned and numeric columns are standardized. PCA is then applied to reduce dimensionality, summarizing the main variation in the dataset into two principal components. The optimal number of clusters is determined using the Elbow and Silhouette methods, followed by K-means clustering on the PCA-reduced data. The resulting clusters are visualized and summarized in a table, providing insights into the distribution of job positions and salary ranges. This workflow allows readers to quickly understand patterns in the NYC Jobs dataset and can serve as a foundation for further analysis, such as investigating salary trends by agency or job type.

Cumple_María_2026

This document contains the solutions to some of the exercises from the 4th chapter of "A First Course in Bayesian Statistical Methods" by Peter D. Hoff. Note that only the solutions requiring code have been posted. The rest are doable or are to be done only by hand.

Análisis de consistencia interna mediante Alfa de Cronbach de un instrumento de 10 ítems Likert aplicado a 155 estudiantes de Ingeniería de Sistemas. Incluye correlaciones item-total, matriz de correlaciones y visualización de distribuciones. α = 0.89