Data Analysis Fundamentals Simulator

Data analysis is the process of turning a scattered pile of raw observations into an actionable insight: a trend, a prediction, a confidence level. This simulator generates a synthetic dataset from a hidden linear relationship plus random noise, then fits a least-squares regression line to it live, computing the Pearson correlation coefficient and R² exactly the way a real analysis would. A configurable share of points is deliberately corrupted into outliers so you can see how bad data drags a fit off course, and how a simple z-score threshold flags the points most worth investigating. Tune sample size, noise, the true trend slope and the outlier rate to build intuition for when a correlation is trustworthy — and when it's just noise dressed up as a pattern.