Significance of Adjusted coefficient of determination
Adjusted coefficient of determination measures how well a statistical model fits the data. It is a modified version of the coefficient of determination (R-squared) that adjusts for the number of predictors in the model. The adjusted R-squared increases only if the new term improves the model more than would be expected by chance. It can decrease if a predictor does not improve the model fit enough.
Synonyms: R-squared, Coefficient of determination, Goodness of fit, Explained variance, Adjusted r2
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The concept of Adjusted coefficient of determination in scientific sources
Adjusted coefficient of determination gauges a model's fit to data, accounting for the number of predictors. It's a modified R-squared that penalizes the inclusion of unnecessary variables in the model.
From: Sustainability Journal (MDPI)
(1) In this study, the adjusted coefficient of determination (R 2 ) was used to evaluate the experimental data and related results to predict the drying process.[1] (2) Is a statistical measure of how well the regression predictions approximate the real data points adjusted for the number of predictors in the model.[2] (3) The adjusted coefficient of determination (adjusted R 2 ) indicates the relationship between the model goal and physiological parameters, with larger values under cold stimulation indicating a stronger correlation.[3] (4) This is a measure of how well the model fits the data.[4]