Sentence examples for model to variance from inspiring English sources

We compared total variance by the full model to variance explained by the parsimonious model to explore the relative contribution of the additional predictors and the potential loss of statistical power due to missing data across all the predictors in the full model.

While Trt1 and Trt3 consistently received very low rank probabilities (e.g., 0.5% chance of being the best), the probability of Trt2 versus Trt4 being the best treatment varied from 71.3% versus 28.2% with the homogeneous variance model to informed variance model to 43.2% versus 56.8% with the unrestricted variance model (see Table 5).

We performed all analyses using Stata 11.2 (StataCorp, College Station, TX) and utilized an ultimate cluster model to estimate variance.

We analyzed the data with the less complex HKY model to minimize variance associated with estimating the additional rate parameters.

The model was then used to determine the contributions of the different levels in the model to the variance about this mean.

Kang et al. and Christian have outlined some of the assumptions that are implicit in using the classical twin model to partition variance into genetic and environmental components.

A random intercept for each hospital was included in the model to split total variance into within-hospital variance and between-hospital variance.

This paper thus extends various well-known results of Dehling–Taqqu and Koul Mukherjee from finite variance long memory models to infinite variance models of the above type.

F is defined as the ratio of the variance explained by the model to the residual variance in the model.

Together, the three pre-admission measures explained around 8% of the variance in both composite scores, increasing, after inclusion of gender, age, ethnicity and deprivation in the models, to 16% variance in the EPM and 12% variance in H&C.

A reaction norm model to estimate genetic variance for macro-environmental sensitivity was combined with a structural model for residual variance to estimate genetic variance for micro-environmental sensitivity using a double hierarchical generalized linear model in ASReml.