Adjusted R-squared\x05-0.012421\x05 S.D.dependent var\x05\x051.61E-05S.E.of regression\x051.62E-05\x05 Akaike info criterion\x05\x05-19.14087Sum squared resid\x051.74E-08\x05 Schwarz criterion\x05\x05-18.95114Log likelihood\x05695.0711\x05 F-statistic\x05\x050.825786Durbin-Watson stat...
The Lankford equation was then compared to four other reference equations using adjusted R-squared ( R 2 ) and Root Mean Square Error (RMSE) as primary metrics to determine correlation with measured C W . Kinematics data collected from reflective markers placed on bony landmarks were compared ...
果: Adjusted R-Squared = 0.8671) 根据中国证券市 场基金评价研究数据库相关数据整理而成 图中实线代表用我们模型对这个实例的计算 结果曲线, 波纹线是这个例子的实际演化曲线, 对 照两者可见拟合较成功. 098901-5 物理学报 Acta Phys. Sin. Vol. 63, No. 9 (2014) 098901 ...
Sum of Squared Residual: 3.237636036555E-16 Correlation Coef. (R): 0.998819161526896 R-Square: 0.997639717433292 Adjusted R-Square: 0.997276597038414 Determination Coef. (DC): 0.997639717433292 Chi-Square: 5.89888356402621E-10 F-Statistic: 1162.36473616939 Parameter Best...
Among all possible regression equations with various values for these coefficients, these are the ones that make the sum of squared prediction errors the smallest possible for these particular magazines. The regression equation, or prediction equation, is Predicted page costs=a+b1X1+b2X2+b3X3=−...
r^2 {/eq} is the squared distance between the two charges. This can be simplified to {eq}\vec F_e = q\vec E {/eq} where {eq}\vec E {/eq} stands for the electric field and q is the test charge by noticing that, in Coulomb's law, {eq}\frac{q}{4 \pi \epsilon_0 r^...
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R-squared Equation-level variance decomposition Bentler–Raykov squared multiple-correlation coefficient Group-level goodness-of-fit statistics for linear SEM SRMR CD Model vs. saturated chi-squared contribution Residual analysis for linear SEM Mean residuals Variance and covariance residuals Raw, normaliz...
We included all links between the variables and applied a two-step approach, using mean-adjusted and variance-adjusted unweighted least squares (ULSMV, Savalei & Rhemtulla, 2013) as the estimator and THETA for parametrization, first examining the measurement model and then the structural model. ...
adj.rr <- summary(res.lm)$adj.r.squared %>% signif(2) AIC <- stats::AIC(res.lm) %>% signif(2) BIC <- stats::BIC(res.lm) %>% signif(2) # Build model equation eq.char <- as.character(signif(polynom::as.polynomial(coefs), 2)) eq.char <- gsub("e([+-]?[0-9]*)"...