Sentence examples for explanation of variables from inspiring English sources

Component analysis is a multivariate statistical technique which can be used for reducing complications of input variables when there is a large volume of information and it is anticipated to have an enhanced explanation of variables (Noori et al. 2010).

For a detailed explanation of variables and data sources, see Appendix 2. We also use these parameters to ensure the representativeness of our sample by comparing the 100 firms selected with the population of all firms listed in the four indices (N = 160).

Table 1 Independent variables used in the study Choice structuring properties Variables Data sources Explanation of variables and data sources Concealability Number of vessels in port http://www.marinetraffic.com (University of Aegean (Greece), Department of Product and Systems Design Engineering) The source provides real-time data on the number of vessels docked at each country's port.

ASA-PS American Society of Anesthesiology physical status explanation of variables, ASA-PS score continuous scale (I V), age categorical (75 ≤ age < 85, 85 ≤ age), types of anesthesia categorical (general anesthesia, general anesthesia combined with neuraxial or regional block, spinal anesthesia), location of fracture categorical (inter-capsular, extra-capsular).

For explanation of variables, see "Methods" in main text.

1 Male = 0; Female = 1; See text for further explanation of variables Based on administrative data, we created affiliation matrices linking each GP to all possible networks.

Notations used below, refer to those used in Figure 1 (see legend of Figure 1 for explanation of variable names).

Moreover, the levels of the constants are high, which confirms the low explanation of variability through socio-demographic variables even if the r-squared statistics are reasonable.

Also, Shyu et al [ 22] examined the factor structure and the contribution of underlying variables to the explanation of variability in MMSE scores of older adults in Taiwan and found that the same three structural factors explained much of the variance among the general elderly population, including those free of dementia.

We identified ten common issues in SEM applications including strength of causal assumption, specification of feedback loops, selection of models and variables, identification of models, methods of estimation, explanation of latent variables, selection of fit indices, report of results, estimation of sample size, and the fit of model.

Some studies did not report the necessary information such as the R 2 or p values of path coefficients (i.e., 22.6% reported R 2, 65.8% reported p value), model modification/validations, nor an explanation of latent variables in SEMs (i.e., none explained the latent variable estimation, 28.1% did not have an estimation method).