Linear regression is explained through fitting a line to data, calculating r squared, and determining p-values. The process involves measuring residuals, understanding variance, and assessing how well mouse weight predicts size. The significance of results relies on both a high r squared and a low p-value, ensuring reliable relationships in data analysis.