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Table 3 shows the factor analysis results of the dependent variable, i.e.: entrepreneurial intention.

These predictors are able to explain 16%and13%13 % of the results of the dependent variable, respectively.

The Ca1 factor is also able to explain 8%% of the results of the dependent variable on the basis of the partial eta-squared value.

To this model was added, for each particular case, the results of the dependent variable before the intervention, up to a total of 9 variables.

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The obtained models reproduced the experimental results of the dependent variables with errors below 6%.

In order to know which of the selected independent variables have the most influence on the results of the dependent variables, the partial eta-squared values have been evaluated (Table 2).

A central composite factorial design was used in conjunction with the software ANFIS Edit MATLAB 6.5 to develop fuzzy neural model that reproduced the experimental results of the dependent variables with errors less than 6%.

We used analysis of covariance to adjust results for the dependent variables (total sleep time, sleep onset latency) measured at baseline and in exploratory analyses for treatment covariate interactions.

Results for the dependent variables show that 89% of respondents used any health services within the past two years, 42% had perceived a need for specialty care after using any health services, and 89% utilized specialty care after having a perceived need for specialty care.

Internal validity analyzes if the collected data in the study are result of the dependent variables and not from an uncontrolled factor.

Tables 10 and 11 of the Appendix present results when the dependent variable is annual earnings and the sample is restricted to men 25 64 years of age, respectively.

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