DAT301 Midterm - Baseball Data

CCA biplot

Nobel Prize API Data Extraction Project Project Overview This project involved extracting and analyzing data from the Nobel Prize API to explore patterns and insights about Nobel laureates and their achievements. Key Components Data Extraction - Connected to the Nobel Prize API to retrieve comprehensive data about Nobel Prize winners - Extracted information including laureate details, prize categories, award years, and affiliations - Processed JSON data and transformed it into a tidy data format suitable for analysis Data Processing - Cleaned and structured the API response data using tidyverse tools - Created organized dataframes with key variables such as: - Laureate names and biographical information - Prize categories (Physics, Chemistry, Medicine, Literature, Peace, Economics) - Award years and prize motivations - Institutional affiliations and countries Analysis Focus Areas Potential areas explored could include: - Distribution of prizes across categories and time periods - Gender representation among laureates - Geographic patterns in prize winners - Age trends of laureates at time of award - Institutional affiliations and their prize frequencies Technical Skills Demonstrated - API integration and data retrieval - JSON data parsing and transformation - Data wrangling with dplyr and tidyr - Exploratory data analysis - Data visualization with ggplot2 This project showcases your ability to work with external APIs, handle real-world data structures, and apply tidy data principles to extract meaningful insights from public datasets.

STAT 427, Chapter 4 Homework, Time series statistics

This report follows Text Mining with R: A Tidy Approach (Silge & Robinson, 2017), Chapter 2 to implement baseline sentiment analysis and then extends it in two ways as required:

Cindy Seungji Lee

Este documento presenta un análisis econométrico aplicado utilizando la base de datos wage1 de Wooldridge. A través de un modelo de regresión lineal múltiple, se examina cómo variables como la educación, la experiencia laboral, la antigüedad, el género y el estado civil influyen en el salario por hora. Además, se incluyen pruebas de supuestos clásicos, análisis de linealidad, residuos y visualizaciones con ggplot2 y car.

Atividade 11 da matéria de computação para análise de dados. Período 2025.2 UFPRE Aluna: Camila de Almeida Professor: ERMESON CARNEIRO