Sentence examples for connection between variables from inspiring English sources

Exact(3)

Figure 1 presents the data structure and the investigated connection between variables.

First, the cross-sectional nature of the data is of generic concern when considering any causal connection between variables, severely limiting our ability to ascertain the temporality of any association shown.

The Kaiser-Meyer-Olkin value, shoving the strength of connection between variables, was 0.82, exceeding the recommended value of 0.6, and the Barletts Test of Sphericity reached statistical significance.

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Studies on model photosynthetic organisms revealed an intimate connection between variable fluorescence and O2 evolution [32], to the extent that it became a common practice to assess the rate of electron flow from the fluorescence parameters.

For experimental validation complex model systems require identification of only a few high-confidence connections between variables, but many common network analysis tools instead generate high-connectivity graphs [ 9] (due to indirect effects).

In the first step, students have to identify the connections between variables in order to acquire system knowledge.

They found that students still had problems to use their gained knowledge to deal with connections between variables.

This agrees with the intuitive notion, that causal influences can only be attributed to existing connections between variables.

Connections between variables are often referred to as causal relations, but the only widely accepted method for declaring causation between two variables is a randomised experiment.

If this assumption does not hold, there may be direct connections between variables that are not causal relations but that are due to other sources, such as common hidden causes.

This information could potentially enable improved structure learning, whether by exploiting the relationship of consecutive samples or by enabling connections between variables that represent regulatory loops as is possible using dynamic Bayesian networks [ 29– 31].

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