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We started with the Full Model (Model 3; Figure 1) because this model included all theoretically interesting causal paths.
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1. SERVQUAL has a higher explanatory ability; thus, it can be considered as the best measurement model because the measurement dimensions in this model include the entire service transmission process from the initial interaction with the customer service website design to the final fulfillment of the service.
Note that a patient's risk score could change over the hospitalization because the model included time-dependent covariates.
Because the model includes a "lagged" measure of presidential approval (that is, presidential approval in the previous quarter), the model's predictions also sometimes lag the president's actual approval rating by a quarter.
Because the model includes trees as parameters, we cannot directly use traditional optimization methods in Euclidean space to find the solution.
Because the model includes student fixed effects, all time invariant characteristics such as race/ethnicity and sex will be captured in the student fixed effects.
Because the model includes Europe-wide weather patterns, it also reveals how far the deadly effects of air pollution can reach.
The flux rates were much higher than the rates of [Ca2+]i change shown in Fig. 2E because the model includes the effects of strong Ca2+ binding in the cytoplasm by endogenous buffers and fluorescent indicators.
Because the model includes a saturation of the number of simultaneous infections for an individual, this feature creates a saturating, non-linear relationship between EIR and force of infection.
For the three-parameter models, HCH-1 provided the best fit because the model includes a fractional coverage parameter which accounts for the number of reactive sites covered by the enzymes.
This should not be seen as strong evidence that PM2.5 absorbance second-trimester levels are more detrimental to birth weight than the whole pregnancy average all the more because the model including both exposure variables did not highlight a clearly stronger association with birth weight for one variable or the other.
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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