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The two models showed strong agreement for the area encompassed by the presence points in the invaded range, and offered insight into the overlapping but slightly different ecological niche occupied by the introduced grass in the invaded range.
The developed models were further validated by using a set of another sixteen compounds as external test set 2 and both models showed strong predictive power with r2Pred = >0.8.
The models showed strong correlation with each other and generally predicted the same patients to be PSA recurrences or systemic progressors.
However, in vivo tests on animal models showed strong antitumor properties in terms of tumor development and metastasis.
Three models showed strong correlation between public-sector logistics data for injectables, oral contraceptives, and condoms and their prevalence rates, demonstrating that current logistics data can provide useful prevalence estimates when timely survey data are unavailable.
Despite the important group differences, all regression models showed strong effects, with the smallest R2 of.24 (predicting SF36 Role Performance with Physical Limitations for the IRMT + seed group).
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All our models showed stronger association than previous equation models (R = 0.468 in the Diabetes Control and Complications Trial and A1c-Derived Average Glucose equations).
It is evident from the result that all models show strong evidence of long-run relationship among the variables.
In the following, we also compute the median model from the K candidate models that is expected to be less influenced by a small number of models showing strong discrepancies.
Furthermore, summed AIC weights across models show strong support for wind speed as an explanatory variable (summed AIC weight = 0.966), but not for any other factor, including mean temperature (summed AIC weight = 0.596).
The proposed model showed strong heterogeneity compared with the initial model at each depth slice.
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CEO of Professional Science Editing for Scientists @ prosciediting.com