Regression and reusable analysis tooling in R

The Data Analysis module split into two pieces, and the second mattered more than the first. First: built and validated multiple regression models against a basketball dataset, log transforms where the raw data didn't meet the model's assumptions, and real assumption checks rather than trusting the fit blindly.

Second: took that workflow and generalized it into a reusable R function, one that automated descriptive statistics, missing-value imputation, outlier detection, and visual summaries for any dataset handed to it, not just the basketball one it was developed against, plotting the linearity checks behind that automation with ggplot2 and GGally together.

That second piece is the part worth noting, treating an analysis workflow as something to engineer and reuse rather than a one-off script written for a single assignment, closer to how a real data team would want a shared utility to behave than how coursework usually gets written.