## 3 Data and Methods
One of the most important derivations started from the lists of declared residences for each respondent and consisted in the last non-blank one, based on which we divided the dataset into three large Romanian historical regions, namely: MB (Moldova and Bucovina or the northeast region), TBCM (Transylvania, Banat, Crisana and Maramures or the central part together with the northwest and the west regions) and OMD (Oltenia, Muntenia and Dobrogea or the south, southeast and southwest regions). To ensure the server-side persistency of all the tests in the first DM round, we have used a 64 bit version of Microsoft SQL Server 2016 x64 Analysis Services (SSAS) running on a Windows 10 x64 virtual machine (inside the VM Virtual Box Manager 5.0.14 from Oracle - Redwood City, California, USA) with 6 GB of Virtual RAM Memory and 2 physical processing cores (half of the total amount) of an Intel Core I7-4710 HQ central processing unit. To effectively create the first mining structures corresponding to the whole of Romania, deploy them on the server-side layer (SSAS) and explore the results, we comparatively used the designer from Microsoft Visual Studio 2017 (SQL Server Data Tools for Business Intelligence also required as distinct component to install), the Excel DM add-in for Office Excel 2013 and specific queries, namely of DMX type DM eXtensions in the traditional Structured Query Language (SQL). In these first DM tests, we have involved the Microsoft algorithms based on both the decision trees and naive Bayes techniques, which belong to the traditional classifiers that have been commonly used in the context of classification problems [93]. The latter also acted as a sort of fields pre-selection method before the next steps of data processing, including renaming, numerical scale derivations and missing values' treatment, which preceded the use of automatic variable selection procedures acting as next rounds DM followed by statistical analysis. To process the variables for this particular region (Romania--those corresponding to all 8 categories of questions above) as well as for a larger one (most of the European countries--just the ones corresponding to the WQ category because of finally testing only the powerful dual-core by induction), we aimed for clear and trustful answers. We were also aware of the traditional procedures of treatment for missing values and their effect on the classifier accuracy [94]. Still, we did not assimilate the missing values, the undecided state of the responses and the unwillingness to answer to a certain state of the original scale. Hence, we generated an extra level (usually a middle one), which cost us a lot in terms of artificially generated variance, but ensured support for a balanced approach with more realistic values for the coefficient of determination (R-squared), accuracy tests and ratios between the magnitudes of the most powerful underlined influences. Therefore, the entire set of treated fields/variables, resulting from the first round DM (Table A1, Appendix A), served as an input for further applying variable selection procedures and regression analysis in Stata 16. For further exploration of the two most powerful common influences (models' core) and particular ones, we have considered a general variable selection technique, namely LASSO (Least Absolute Shrinkage and Selection Operator), originally documented in geophysics as a L1-regularization approach for a problem called sparse spike deconvolution [95; 96; 97].