Sentence examples for by the common factor from inspiring English sources

In the factor model, the item variance is decomposed in to a part due to (explained by) the common factor(s) and a residual part.

For example, two sets of factors { f1} and { f1,  f2} are partially overlapped by the common factor f1.

The common animal effect can be interpreted as the breeding value for the latent common factor whereas the specific animal effects are the environment-specific remnant breeding value unexplained by the common factor.

The proportion of genetic variance explained by the common factor was only 26.20% for PE and ψ ^ was high in PE (2606.73), which showed that much of the additive genetic variance was not accounted for by the latent common factor.

MI criteria are: (1) equality of factor loadings over groups; (2) equality of item intercepts over groups (i.e., differences in item means are the result of differences in factor means), and (3) equality of residual variances (i.e., variance in the observed variables, not explained by the common factor) over groups.

Over 50% of the overlap between the four measures was accounted for by genetic effects (see Table  4).> -wrap-foot> * The first number is the variance explained by the common factor, the second number is the variance explained by the measurement specific influences of D There are several constructs reflecting subjective wellbeing.

Then, by omitting the common factor e i k z−i ω t, RCP wave can be expressed as (frac {F}{sqrt {2}} (hat {x}-ihat {y})) and the LCP wave (frac {F}{sqrt {2}} (hat {x}+ihat {y})) where F is the field amplitude.

† indicates t-value <3, †† t-value <2 Residual covariances measure what two variables have in common that is not captured by the common factors (Table 6).

Communalities (proportion of variance of the variable that is accounted for by the common factors) were calculated to assess the factor structure.

For the PSP-group large residual variances for Posture and for Head & Neck Rigidity indicated that much variability in these variables could not be captured by the common factors.

Correlation analysis showed that ACh, Aβ42, P-tau, and IL-6 levels in the CSF of diabetic rats were not linearly related (P > 0.05), while further data analysis showed that ACh change and Aβ42, P-tau, and IL-6 were highly negatively correlated, suggesting that changes in ACh in diabetic rats were determined by the common factors in Aβ42, P-tau, and IL-6 variation.