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Comparing each of these values with the respective ones of the BPT model, the estimates of the Poisson models are about two times larger than those of the BPT model.
Although correcting the variance and corresponding test statistics for overdispersion is a standard procedure in the Poisson model, the estimates of the regression parameters are inefficient because they have more sampling variability than is necessary.
In a multivariable model, the estimates remained unchanged.
When entered simultaneously into the model, the estimates were unstable, thus, they were entered separately.
By keeping such a variable in the model the estimates of age are at risk of being incorrect.
When including ethnic group rather than region in the final model the estimates changed less than 10% for all parameters assessed.
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In the multivariate model that includes peak arsenic levels (full model), the estimate is 0.86 (0.86–1.0.86
In particular, for the Weibull model, the estimate is large (p ^ = 0.98).
Using a mixture model, the estimated probability for teams to play strategically is 62%.
In stacking terminology, this model is referred to as a meta-model: log T k = ∑ b = 1 B η b H b (r k ) + ε (5) Here, { η b} are unknown coefficients, which are estimated by the meta-model; the estimates are the values that best relate the variables { H b } b = 1 B to the targeted outcome/response.
We looked at residuals to check for discrepancies between the proposed model and the estimated model.
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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