Sentence examples for model is log from inspiring English sources

The duration of lag2 in this model is log(2)/ μ + 1/ ν ~ 3 h, which is shorter than the observed arabinose lag2 phase duration, ~5 h.

In mathematical terms, if we denote by P the prevalence, the fitted model is: log ⁡ (P (1 − P ) ) = α + β 1 X 1 + β 2 X 2 exp(β1) (respectively exp β2)) is the Odds Ratio by unit of X1 (respectively X2), that is the number by which the disease odds is multiplied if exposure X1 (respectively X2) increases by one unit.

Since the crash prediction model is log-linear, crash precursors are categorized.

While step Four is time-consuming, steps One and Three are quick as the model is log-linear in the remaining parameters.

We believe that the advantages of the log-binomial method with the likelihood ratio test substantially outweigh those of the Robust Poisson when the true model is log-binomial.

Briefly, the control model is log-linear in 14 indicator variables for 5 year age-at-exposure categories, an indicator for gender, and the numerical variable log mean attained age, each of which is well-defined for the data cells.

The model was log(WO) = sex*age*log wAlbB) + sex*age*log wAlbA) + population, where WO, wAlbA and wAlbB are the quantification of the corresponding element (quantitative variables); the values where log-transformed to ensure normality of the residuals; * indicates that the model considers both the main and interaction effects of the variables.

The final multiple logistic regression model was: log(P cancer)/(1− P (cancer))= b0+age+sex+pack-year_quartiles+mEH3+mEH4.

The final model was log(k) = −0.609 − 0.013 × L∞, and the standard deviation of the residuals was 0.305.

The TAG model was log-transformed and expressed as for the variance analysis.

The model was log-THg = LC + LC + year + year*LC + year*LC (where LC = fish length centered); backward stepwise regression was used to identify terms significantly related to log-THg in each lake (see Supporting Information).