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The predicted probability that observation i belongs to class j is given by the variable p ij.
The Moore-Penrose inverse matrix was defined by the variable P from Eq. (7).
Table 2 presents the overall multidimensional logistic regression model identifying the strongest (as defined by the variable p value <0.10) predictors of not working 3 months after injury.
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The production rates are symbolized by the variables p n and p d, whereas μ and ρ represent decay rates.
The state of each branch is governed by the variables p ik(s), which is the probability that branch i is in state k at time s in the past.
Start by building model M B with the variables P s and P F. The subset of variables not included in M B is divided into user-defined fixed-sized groups of variables: P a.
It is unknown, however, how the observed quantity of labile P is affected by the release of variable P species.
The statistical significance of the relationship between fellowship interest and overall medical knowledge was also preserved albeit lessened by the adjusted variables (p = 0.04).
The graphical model in Figure 1 denotes the prior probabilities of GO term activity by variable P, with P Gk = 1| P) denoting the prior of GO term activity and P Gk = 0| P) the prior of being dormant.
In the final model the classification was improved by eliminating the authority centrality variable, p =.451, betweenness centrality, p = .329, degree centrality, p =.454, eigenvector centrality, p =.854, hub centrality, p = .329, were also dropped due to their high residual probability.
In LN− tumours, histological grade was the most significant variable (p < 0.001), followed by tumour size (p = 0.027) and ER status (p = 0.183).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com