Good model
- Input
- R² = 0.92
- Expected output
- 92% da variância explicada
But checking residuals is still mandatory.
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r-squared and regression model quality
R² = 1 − (SSR / SST). An R² of 0.80 means 80% of Y's variance is explained by X in the linear model.
But checking residuals is still mandatory.
Identical R² for completely different patterns, always visualize your data.
Simple linear regression is a statistical model that describes the relationship between an independent variable X and a dependent variable Y as a line, Ŷ = β₀ + β₁X. The slope β₁ tells you how much Y changes for each one-unit increase in X, and the intercept β₀ is the predicted value of Y when X is zero. It is the foundation of much of data analysis, used both to understand a relationship and to predict values.
It depends on the field. In physics: R² > 0.99 is expected. In social sciences: R² of 0.30 may be considered strong. There is no universal threshold.
No. It helps explain the scenario and use the tool more safely, but real decisions should consider official sources, full context and qualified guidance when needed.
One x, y pair per line. Maximum 200 pairs.
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