The speedy increase in wind parks has brought to light a plethora of conflicts. Despite their benefits, there are more than a few who are opposed. The goal of this research is to study and evaluate the causes, so that such a project can take place in conditions of cooperation, mutual interest and profit for society and investors. The method chosen was a survey with approximately 600 responses in Attica, continental Greece and the islands. The data collected were analysed via SPSS on three levels: (i) descriptive statistics, (ii) binomial logistic regression to model the attitude towards wind farms, and (iii) factor analysis to identify latent factors that influence people’s thoughts. The results show that the NIMBY effect has no significant influence on the acceptance of the project, contrary to expectations. Moreover, the acceptance of a project does not seem to be a class issue, as the analysis showed that attitude is independent of income. The frequency and logistic analysis showed as the main determinants of the public attitude: (i) in a positive way, open and continuous briefing during all the construction stages and minimisation of greenhouse gases emissions (i) in a negative way, the impact on flora and fauna and the lack of trust in the investors.

The analysis of multivariate data is a central issue in biomedical research, where the accurate classification of patients and the extraction of reliable conclusions are of critical importance. Linear Discriminant Analysis (LDA) remains one of the most established methods for both dimensionality reduction and classification of data. In this paper, we examine in detail the theoretical foundations, assumptions, and statistical properties of LDA, and apply the method step by step to real data from the Breast Cancer Wisconsin (Diagnostic) database, which includes cellular features from breast biopsy samples with the aim of distinguishing benign from malignant tumors. Emphasis is placed on the importance of the method’s assumptions, such as multivariate normality, equality of covariance matrices, and absence of multicollinearity, demonstrating that their fulfillment leads to significant improvements in model performance. Specifically, careful preprocessing and strict adherence to these assumptions increase classification accuracy from $95.6\%$ ($94.7\%$ cross-validated) to $97.8\%$ ($97.4\%$ cross-validated). To our knowledge, this study is the first to demonstrate the dual use of LDA as both a dimensionality-reduction tool and a predictive classification model for this medical database within the same biomedical analysis framework. Moreover, we provide, for the first time, a systematic comparison between our assumption-aware LDA model and related studies employing the most accurate machine-learning classifiers reported in the literature for this dataset, showing that classical LDA achieves accuracy comparable to these more complex methods. The resulting discriminant model, which uses 13 variables out of the original 30, can be applied easily by clinical researchers to classify new cases as benign or malignant, while simultaneously providing interpretable coefficients that reveal the underlying relationships among variables. The implementation is carried out in the SPSS environment, following the theoretical steps described in the paper, thus offering a user-friendly and reproducible framework for reliable application. In addition, the study establishes a structured and transparent workflow for the proper application of LDA in biomedical research by explicitly linking assumption verification, preprocessing, dimensionality reduction, and classification.

The growing generation of household hazardous waste (HHW) presents critical environmental and public health challenges worldwide. This study investigates prevailing trends in HHW management and analyzes the socio-economic and demographic determinants that influence public perceptions, attitudes, and behaviors toward HHW recycling practices. A comparative mass flow analysis is also conducted to evaluate the limitations of current HHW management practices in Greece and outline policy implementation plans. Statistical findings indicate that income significantly influences recycling behavior. Individuals with annual incomes between €10,001 and €30,000 are less likely to engage in HHW recycling, whereas those earning over €70,000 demonstrate higher levels of recycling participation. The public recognizes the need for green collection points for appropriate HHW management. However, no statistically significant correlation is found between income levels and perceived importance of these facilities. This outcome is attributed to the high proportion (46.7%) of dichotomous variables in the χ² independence test, exceeding the recommended threshold of 25%, which limits interpretability. Such findings indicate the complex interplay of behavioral and socio-economic variables in HHW recycling. The study highlights the importance of targeted public policies, educational interventions, and infrastructure improvements to increase recycling participation and promote sustainable HHW management in Greece.