r^2 = 16

Sat, 18 Apr 2026 14:28:40 +0000

# Understanding R² = 16: A Deep Dive into Statistical Significance in Regression Analysis In the world of statistics and data analysis, the coefficient of determination, commonly denoted as R² (R-squared), is one of the most important metrics for evaluating the performance of regression models. But what happens when R² = 16? At first glance, this might seem unusual since R² values range from 0 to 1 in standard linear regression. However, with a deeper look into scaled or transformed models, R² = 16 can indicate meaningful explanatory power — and understanding how this happens is key to making informed decisions based on regression results. ## What Is R² in Regression Analysis? R-squared measures the proportion of the variance in the dependent variable (the outcome you’re predicting) that is predictable from the independent variables in your model. It tells you how well your model fits the observed data — with values closer to 1 indicating a stronger fit.