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22 de abr. de 2022 · Learn how to calculate and interpret the coefficient of determination (R²), a measure of how well a statistical model predicts an outcome. See examples, formulas, graphs, and rules of thumb for simple linear regressions.
R-squared is a measure of how well a linear regression model fits the data. Learn its definition, limitations, and how to visualize it with examples and graphs.
In summary, the R square is a measure of how well the linear regression fits the data (in more technical terms, it is a goodness-of-fit measure): when it is equal to 1 (and ), it indicates that the fit of the regression is perfect; and the smaller it is, the worse the fit of the regression is.
En estadística, el coeficiente de determinación, denominado R² (se pronuncia R cuadrado ), es un coeficiente usado en el contexto de un modelo estadístico cuyo principal propósito es predecir futuros resultados o probar una hipótesis.
Learn how to interpret the r-squared value, which measures how much of the variation in the response is explained by the predictor in simple linear regression. See examples, formulas, and screencasts with skincancer.txt data.
Learn how to calculate and interpret r-squared, the proportion of variation in the response variable explained by the explanatory variable in linear regression. Watch a video and see examples, questions and comments.
Coefficient of determination (R-squared) indicates the proportionate amount of variation in the response variable y explained by the independent variables X in the linear regression model. The larger the R-squared is, the more variability is explained by the linear regression model.
