R^2 - RC + C^2 = 2000 - 800 = 1200

Wed, 06 May 2026 02:34:30 +0000

Understanding R² in Statistical Analysis: How R², RC, and C² Relate to Statistical Performance (Explaining 1200 as a Performance Benchmark) In statistical modeling and data analysis, the R² value (coefficient of determination) is one of the most widely used metrics to assess how well a model explains the variation in a dependent variable. But sometimes, formulas or comparisons involving R² appear in contexts that may seem abstract—like the equation R² – RC + C² = 2000 – 800 = 1200. At first glance, this algebraic statement may appear cryptic, but unraveling it reveals key insights into model evaluation and diagnostic metrics. ### What is R²? The R² (R-squared) value measures the proportion of variance in the dependent variable (Y) that is predictable from the independent variable(s) (X) in a regression model. It ranges from 0 to 1 (or 0% to 100%), where values closer to 1 indicate a strong explanatory power of the model.