Do certain positions in baseball have a higher correlation to errors? Are there factors that determine if a player will play 80 or more games in a season? Does a player having more Gold Gloves and All-Star game appearances put them in special company in the Baseball Hall of Fame? Using a logistic regression model, this analysis explores which factors are most correlated with a fielder being error-prone. A principal component analysis (PCA) is then used to show that a player’s offensive output (hits, runs, RBIs) is closely tied to how many games they play in a season – i.e. whether they are an everyday “workhorse.” Finally, a K-Means clustering model groups Hall of Fame players by All-Star appearances and Gold Gloves won, revealing three distinct types of Hall of Famer. These models were built in R using the publicly available Lahman package (Friendly, Dalzell, Monkman, Murphy, Foot, & Zaki-Azat, 2020), which was cross-referenced against Baseball Reference for validation. The tables used were Fielding, Batting, People, HallOfFame, AwardsPlayers, and AllstarFull.

Actividad 122 basada en la base de datos de la actividad 112 - Teoria de probabilidades ÙJ

Trabajo analisis de base de datos con rstudio y excel, actividad 121 TP PUJ - Jose Villa

Math problems of the day for 8/10/26.

This module will introduce you to loading and manipulating the data from the breast cancer METABRIC dataset, visualizing and working with*gene expression measurements, and building predictive models based on the expression of many different genes and the associated clinical data.