This assignment explores Chapter 6 of An Introduction to Statistical Learning with Applications in R, focusing on linear model selection and regularization. The chapter introduces methods for improving model accuracy and interpretability by selecting an appropriate subset of predictors or constraining coefficient estimates.

Summary of baseball and soccer players stats.

Big idea: data can hide patterns Large biological datasets are often too big to understand by reading numbers in a table. In DREAM-High, we will eventually use heatmaps to look for patterns in breast cancer gene expression data from patients in The Cancer Genome Atlas. A heatmap can help us ask questions such as: Which samples look similar to each other? Which genes behave similarly across patients? Can visual patterns help us discover tumor subtypes? Today we will learn the same basic idea using a small practice dataset that comes with R. Main idea: A heatmap turns numbers into colors so that hidden structure becomes easier to see.

This R notebook explores the "Tax Revenues as Share of GDP" dataset from Our World in Data.