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Figure 5 Factors ranked based on their predictive power (i.e., beta coefficient value of the model) to retweeting behavior.
The top four factors, ranked by faculty, were perceived usefulness, attitude toward e-learning, program flexibility, and clear direction.
Information about risk factors ranked as the most important category overall, followed by information on cardiac anatomy and physiology, medications, and physical activity.
The influence on fiber yield in the two year tests by the four factors ranked as follows: X3>X4>X1>X2.
The factors ranked 1 2 were selected for optimization since they are the ones with the greatest effect on the response variable, namely ethanol production.
Least important factors (ranked 1 2) were: patient's socioeconomic profile (88%%), religious convictions (81%%), drug abuse (79%%), severe psychiatric illness (78%%).
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The three factors rank in importance as properties of commodity > design of bottle shape > label design.
Of the 300 downregulated genes in this shared signature, FOXA1, GATA3, and ESR1 were among the top ranking transcription factors (ranks 10, 3 and 1, respectively).
This factor ranked as 6th among the factors.
Table 4 shows that trusting the social network members is the factor ranked highest by the experts as causing users to poorly judge social engineering-based attacks in a social network context.
The output of this step is the identification of risk factors in the final predictive model, with each risk factor ranked as strong, regular, or weak.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com