The new methodology improves on this model by predicting expected incidence based on many covariates, including mortality.
We did so by using the final model to predict expected values for each observation if percent Latino equaled zero, as described in Greenland and Drescher (1993).
Using this final stratified model to predict expected values, we estimated that arsenic levels in CWSs with 100% home ownership would be, on average, 3.1 μg As/L lower, compared to CWSs at the mean.
Using the final model to predict expected values, we estimated that among small systems, nitrate levels for CWSs with 0% Latinos would be, on average, 6 mg NO3/L lower compared with CWSs at the mean.
While it is impossible for any model to always predict expected rates higher than observed rates, as no statistical model can be 100% accurate, a good model should do this infrequently.
Although chromosome pairing models are useful for predicting expected gametic ratios and genotype frequencies, adherence to expectations will vary among loci according to three conditions: the proximity of the locus to the centromere; the particular chromosome pair being considered; and how strictly a particular chromosome associates with its homolog.
For exercises that consider physical outcomes of armed conflict, detailed models may be used to predict expected outcomes (for example, one can model, with high degrees of accuracy, the effectiveness of a particular type of weapon against a particular type of target).
In the paper a time-variant probabilistic model was presented to predict expected costs of repair and replacement which was then used to calculate life-cycle costs for RC structures in marine environments under different exposure conditions.
The model is successful in predicting expected output current levels in a sub-100-nm-channel CNT transistor experimental data.
The regression model presented here allows us to predict expected deposition at a given distance.
