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Furthermore, in our regressions, we chose not to include interactions aiming at easing interpretation of results.
Moreover, similar to LDA, logistic regression provides a deterministic model yielding weighting factors for each contributing variable, easing interpretation and clinical application of results compared with neural network models (which can also more easily over-fit the data).
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We divide them in three categories to ease interpretation.
We fit a second-order CFA model to ease interpretation of the scale and increase parsimony.
Power BI is an influential analytical tool offering a friendly interface, whereby interactive visualization can be easily created to ease interpretation and do efficient reporting.
To improve model fit, outcome variables were natural log transformed and then the regression coefficients were back transformed to original scale to ease interpretation.
To ease interpretation, data is generally presented as the cumulative amount in the entire GIT, e.g. the sum of the four sections in each animal.
To ease interpretation, all regressors are centred around their sample averages, so that π1 can be interpreted as the average unexplained gender pay gap in the full sample.
Continuous predictors were mean-centered to reduce multicollinearity and to ease interpretation of interaction terms.
To ease interpretation only non-transformed results are presented.
Use of explicitly defined and consistent definitions will ease interpretation of future prevalence estimates.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com