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For each individual with known covariate history, one can, with an estimated model, estimate the probabilities of dying on each 4-weekly interval in an imaginary case of his or her optimal adherence.
The coefficients of a linear model estimate the average contribution per affected joint to the serum concentration of a biomarker.
The exponentiated regression coefficients from this model estimate the rate of change in the STAI scores for a one unit change in each independent variable.
The item calibrations from the 1PL model estimate the relative location of the items in relation to the underlying latent construct of food security/food insecurity.
The computations related to period I and III in Section A surveillance case study took approximately 4 hours per time period to fit the model, estimate the parameters and compute the real‐time probabilities.
We may then use standard interval mapping (Lander and Botstein 1989) or an approximation such as Haley Knott regression (Haley and Knott 1992) to fit the model, estimate the parameters μ i, α, δ, and σ, and calculate a LOD score, LOD π, where π denotes the partition of the taxa and λ denotes the location of the putative QTL.
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The estimation model estimates the IMEP of each cylinder.
The Kaplan model estimates the rate of infection.
The model estimates the fraction of the overall variation in the data.
The ANN model estimates the surface roughness with high accuracy compared to the multiple regression model.
The results of the bivariate probit model estimating the propensity scores are given in Table 6.
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