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The correlation between key variables of principal factor 2 suggests the following results.
Observing correlation between key variables of a principal factor can help to control the principal factor.
The result for the correlation between key variables of each factor are given in Appendix 1: Table 12.
Quantitative data were analysed using STATA® version 10. Cross-tabulations with Chi-square and Fisher's exact tests were done to test the associations between key variables of interest.
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Univariate analysis was conducted to examine factors associated with the identification of source patients for young children and to investigate associations between key variables and the identification of refuted patient pairs.
Despite this, efforts to increase participation and limit attrition will be important in ensuring adequate statistical power to examine associations between the key variables of interest.
In the present study, we aim to address the gap in research on the inter-relationships between the key variables of knowledge, attitudes, self-management and QoL by testing a model describing these inter-relationships, as well as considering the influence of age and duration of diabetes on the relationships, among people with T2DM.
A scenario is built by (1) selecting a value for each key variable; (2) estimating the resulting interactions between key variables, dependent variables, and assumptions; and (3) developing a narration describing the future under this set of conditions.
Table 3 Pearson Correlations between Key Variables 1 2 3 4 5 6 7 8 9 10 11 1.
Interactions between key variables were examined as well.
Significant positive correlations were observed between disease incidence and key variables of shoot vigour and vine capacity: total leaf number, leaf dry matter, leaf layer number, leaf area per metre of row, pruning mass and nitrogen accumulation.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com