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Conceptual models and predictors of choking under pressure (i.e., choking) have been proposed, but the role of fear of negative evaluation remains largely unknown.
The GLM procedure was used to compute R-squared values (giving the amount of variation in price explained) for models and predictors.
In order to assess the robustness of models and predictors, bootstrap and permutation analyses were run with 100,000 permutations for some of the above models (see results in Figure 3).
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Deforestation and forest regrowth are tested using logistic regression analysis and an information criterion approach to rank the models and predictor variables.
As regards Factor 2 (Dangerous Errors), our model and predictors can account for 13.3 % of the variance of this variable.
For the validation of this model, an independent validation set consisting of 169 persons was analysed using the same model and predictors.
In the interest of constructing models and selecting predictors, two-thirds of the original set was assigned to training sets.
We used this subset to describe the patient population, evaluate risk factors for death by using logistic regression models, and assess predictors of time from onset to diagnosis and onset to discharge by using Cox proportional hazards models.
A backward selection method was performed for model fitting and predictors with the highest p-value were taken out from the model one at a time until achievement of the most parsimonious model.
Such analysis will require more detailed and extensive comparison of the various model configurations and predictor combinations in ANN.
Changes in self-efficacy were modeled using Hierarchical Linear Modeling, and potential predictors of change were assessed.
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