This project explores the tuition equity policies of Michigan's public universities. It analyzes a dataset containing detailed information about in-state and out-of-state tuition costs, financial aid, SAT/ACT scores, enrollment numbers, and more. The goal is to evaluate the impact of various factors, such as financial aid percentages, equity policies, and regional variations, on tuition prices and institutional support for students. Key techniques employed include: Exploratory Data Analysis (EDA) using visualizations such as histograms, boxplots, and scatter plots to understand relationships and distributions within the data. Predictive Modeling with regression techniques to estimate tuition costs based on various predictors. Classification Models to analyze factors influencing the adoption of tuition equity policies, using random forests and multinomial logistic regression. Clustering to identify groups of universities based on common characteristics, including test scores, tuition, and enrollment data. The results of this analysis provide valuable insights into how tuition equity policies vary across Michigan's public universities and their potential implications for students.

Time Series Modeling Homework 6 by Katie Ziga

Homework 6 Times Series Modeling

Problems 6 and 10

Laboratorio 2 - Métodos Cuantitativos para el Análisis de la Información realizado por Juan Chacon