Significance of Explained variance
Explained variance is a term used to indicate the proportion of variability in estimated adherence that can be attributed to a specific model. In the initial model, a low value of explained variance was observed, suggesting that the model did not effectively account for the variability present in the data. This highlights the importance of refining models to better capture the factors influencing adherence.
Synonyms: Explained variation, Variance accounted for, Coefficient of determination, Explained variability, 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 Explained variance in scientific sources
Explained variance indicates the percentage of variability in estimated adherence explained by a model. A low explained variance in the initial model suggests limited effectiveness in capturing adherence variability.
From: Sustainability Journal (MDPI)
(1) Explained variance represents the proportion of variance in the dependent variable that can be predicted from the independent variables, indicating the strength of the relationship between the variables, and it helps to understand the impact.[1] (2) The text states that the objective of PLS-SEM is to maximize the explained variance of the endogenous latent construct, indicating the amount of variability in the dependent variable that can be predicted.[2] (3) The text discusses measures of "explained variance" in nonrecursive structural equation models, suggesting the importance of assessing the explanatory power of statistical models.[3] (4) Explained variance indicates the percentage to which the dependent variables’ changes are elaborated by the independent variables, with R 2 coefficients indicating the effect of an exogenous variable on an endogenous variable.[4] (5) The Religious dimension included an explained variance of 68.99%, indicating it was the most important motivational dimension compared to the others.[5]
From: International Journal of Environmental Research and Public Health (MDPI)
(1) Explained variance, denoted as R squared (R 2), indicates the proportion of variance in a dependent variable that can be predicted from an independent variable or variables.[6] (2) This variance (R^2) is calculated for employee-level and organization-level, indicating the proportion explained by predictors in the model.[7] (3) It refers to the proportion of variability in the outcome variable (stress) that is accounted for by the predictor variables (resources, employment status) in the regression models.[8] (4) The proportion of variation in a data set that is accounted for by a statistical model, which together had this of 47.80%.[9] (5) Explained variance refers to the amount of variability in the outcome variable that can be accounted for by the statistical model; the lasso model explained 5.86%.[10]
From: Religions Journal (MDPI)
(1) The explained variance was R 2 = 0.08 relative to R 2 = 0.10 in the model with nine SPTs in Table 2, showing the proportion of variance in the dependent variable that can be predicted from the independent variables.[11] (2) The text refers to explained variance, suggesting that it is the proportion of the total variation in a dependent variable that is accounted for by the independent variable(s) in a regression model.[12] (3) Explained variance follows the same pattern as factor loadings, with a minimum of 42% in the indicator of religious experience and a maximum of 79% in the indicator of public practice.[13] (4) The explained variance, such as step 2 explaining 3.5% of the variance, refers to the proportion of variability in a data set accounted for by a statistical model.[14] (5) Explained variance accounted for 67% by the four main factors identified through exploratory factor analysis of the resulting 26 items, indicating the factors' significance.[15]
From: African Journal of Primary Health Care and Family Medicine
(1) This term was used to describe the proportion of variability in estimated adherence that could be accounted for by the model, with a low value observed in the initial model.[16]