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The algorithm involved (Gaussian Mixture Models) was: p X | λ = ∑ i = 1 M w i g X | μ i, ∑ i.

Because of the accuracy of phase identification and picking, the initial P travel times are the most extensively used, and most short-wavelength models are P models.

The significance probability for the multivariable models was P < 0.05.

The threshold of statistical significance in linear and logistic models was P < 5.5 × 10−3 given the nine traits tested.

The Type I error rate for the univariate models was p < 0.10, and p < 0.05 for the multivariable model, as per standard hierarchical modeling approaches.

For all three beverages, the likelihood ratio test comparing NBR with Poisson regression models was p > 0.0001, strongly suggesting that NBR modelling was more appropriate than Poisson regression modelling [ 35].

The significance of the whole model is p = 0.000.

Therefore, the nonlinear dynamic model is P ′ t = Q μ.

The probability distribution of Gaussian background model is: p x = 1 2 π σ e x − μ 1 2 σ (1).

Therefore, the provider's profit using an on-demand model is: P od = C o − β E o ∑ i = 0 N t i.

The model p-value of the causal model is p = 0.75 and the RMSEA is ≤ 0.001.

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