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22 de abr. de 2022 · You can choose between two formulas to calculate the coefficient of determination ( R ²) of a simple linear regression. The first formula is specific to simple linear regressions, and the second formula can be used to calculate the R ² of many types of statistical models.
R-squared is a goodness-of-fit measure for linear regression models. This statistic indicates the percentage of the variance in the dependent variable that the independent variables explain collectively.
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.
In short, the " coefficient of determination " or " r-squared value ," denoted r2, is the regression sum of squares divided by the total sum of squares. Alternatively, as demonstrated in this screencast below, since SSTO = SSR + SSE, the quantity r2 also equals one minus the ratio of the error sum of squares to the total sum of squares:
In linear regression, r-squared (also called the coefficient of determination) is the proportion of variation in the response variable that is explained by the explanatory variable in the model. Created by Sal Khan.
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.
