Significance of Adjusted R 2
Adjusted R 2 is a statistical measure that quantifies the proportion of variance in the outcome variable explained by the predictors in a study, while accounting for the number of predictors used. It provides a refined understanding of the relationship between the predictive variables and the outcome by adjusting for the number of predictors, making it a valuable tool in regression analysis to avoid overfitting.
Synonyms: Adjusted coefficient of determination, Adjusted r-squared
The below excerpts are indicatory and do represent direct quotations or translations. It is your responsibility to fact check each reference.
The concept of Adjusted R 2 in scientific sources
Adjusted R² is a statistical metric that quantifies the proportion of variance in an outcome variable explained by predictors, while accounting for the number of predictors used in a study, ensuring a more accurate assessment of model performance.
From: Sustainability Journal (MDPI)
(1) The adjusted R 2 is used to assess the goodness of fit for different models, and the spatial lag model is shown to have the highest adjusted R 2 in this analysis.[1] (2) A statistical measure that represents the proportion of variance in a dependent variable explained by independent variables in a regression model, adjusted for the number of variables.[2] (3) It is a modified version of another statistical measure that accounts for the number of predictors in a model, providing a more accurate assessment.[3] (4) The adjusted R 2 is a statistical measure used to determine the proportion of variance in the dependent variable that can be explained by the independent variables in a regression model.[4] (5) Adjusted R 2 is used as a metric to indicate the proportion of variance in the dependent variable that can be predicted from the independent variables in the regression model.[5]
From: International Journal of Environmental Research and Public Health (MDPI)
(1) The adjusted R 2 = 0.35 shows that the dimensions of social support can explain 35% of the variation in surface acting.[6] (2) Adjusted R 2 values were reported for the models in each country, representing the proportion of variance in behavioral intention that is explained by the independent variables, while accounting for the number of predictors in the model.[7] (3) Adjusted R 2 values in the hierarchical regression models indicate the proportion of variance in identity diffusion explained by the predictor variables, providing insight into model fit.[8] (4) Adjusted R 2 was used to measure the proportion of variance in BMI explained by the model, indicating how well the model fits the data.[9] (5) Adjusted-R 2 was used as a marker for model precision, consistent with earlier investigations.[10]
From: Asian Journal of Pharmaceutics
(1) A value of 0.9960 was recorded for this metric of swelling in the reported research.[11] (2) This refers to a statistical measure of how well a model fits the data, adjusted for the number of variables in the model.[12] (3) It is a statistical measure for goodness of fit in regression analysis.[13] (4) This plateaus when insignificant terms are added to the model, and the predicted R 2 will decrease when there are too many insignificant terms.[14] (5) A statistical measure of how well the model fits the data, accounting for the number of variables.[15]
From: The Malaysian Journal of Medical Sciences
(1) This is a statistical measure that indicates the proportion of variance in the outcome variable that can be explained by the predictive variables, adjusted for the number of predictors.[16] (2) A statistical measure indicating the proportion of variance in the outcome variable that can be explained by the predictors in the study, adjusted for the number of predictors used.[17]
From: South African Journal of Physiotherapy
(1) This statistical value indicated the degree to which the model accounted for the variance in the data, as outlined in the text.[18]
From: Journal of Public Health in Africa
(1) A statistical measure indicating the proportion of variance in the dependent variable explained by the independent variables in a regression model, found to be 0.427.[19]