Bivariate data involves two variables analyzed together. Correlation: measures linear relationship strength (-1 to +1). Covariance: measures joint variability, sign indicates direction. Pearson correlation coefficient: r = Σ[(x-x̄)(y-ȳ)] / √[Σ(x-x̄)² Σ(y-ȳ)²]. Key concepts: positive (both increase together), negative (one increases, other decreases), zero (no linear relationship). Common traps: correlation ≠ causation, outliers affect r significantly. Exam tips: calculate means precisely, double-check sign. Time-saving: use shortcut formula: r = (n Σxy - Σx Σy) / √[(n Σx² - (Σx)²)(n Σy² - (Σy)²)]. Interpretation: r > 0.7 strong positive, r < -0.7 strong negative. Applications: trend analysis, forecasting, quality control. Scatter plot: visualize relationship and identify outliers. Understanding correlation essential for advanced statistics. Practice calculations with different datasets.