The values linked to population elements are random variables.
Thus, in the standard case, auxiliary data are available for all population elements.
Therefore, the probability of all population elements belong to the same sample is unknown.
The values that are linked to the population elements are fixed.
Consequently, several measures to overcome that problem were envisaged, namely, a large GA population with elements, the crossover of all population elements and the adoption of elitism, a mutation probability of 10%, and an evolution with iterations.
The subjects were selected through a non probabilist approach by convenience, so that the probability of all population elements belong to the same sample is unknown.
The participants were selected through a non-probabilistic approach by convenience, i. e., the probability of all population elements belong to the same sample is unknown.
In the case of hybrid inference, expected values and variances are derived by considering both the design through which auxiliary data were collected and the model used for predicting values of population elements based on the auxiliary data.
The estimates are random variables due to the random selection of samples, i.e., the estimators produce different values depending on which population elements are included in the sample.
Regression estimators relate an auxiliary variable, which is measured or known for all population elements, N, to a variable of interest, which is assessed on a sub-sample of size n.
