Quality of life and social welfare are objectives of the social policy of any state. The study aims to identify the influence of factors such as the circular economy, digital innovation, sustainable entrepreneurship on social progress and completes the current approach identified in the literature by assessing the dependencies between the phenomena represented by them. The quantification of the influences of the enumerated factors on social progress was achieved by identifying some synthetic indicators, such as composite indices, which would surprise the complexity of the analyzed phenomena. To measure the progress of the transition to the circular economy – using multivariate analysis methods – a composite indicator has been proposed and determined that allows the ranking of EU states according to its orientation, as a premise of social progress, and can substantiate the adjustment national policies. The integration of the proposed indicator in the regression models used, with similar indices, is done to highlight the impact of the circular economy, innovation and sustainable entrepreneurship on social progress. Thus, the adaptation of digital technologies in current business models, the development of sustainable innovative entrepreneurship support the transition from the linear economy to a circular economy and offer new study opportunities.

When we mention social inclusion, most of us think of political participation, social rights, civil liberties, equal access to race, ethnicity and gender, access to social services and the labour market, basically to a broader concept than social development. Social inclusion is a concept that can actually be defined, which means it can probably be measured. On this basis, a continuous effort is being made to measure the social inclusion elements, so the results can be used to build new indicators that help measure the multiple dimensions of social inclusion: The Social Inclusion Index, the Human Opportunity Index. This paper presents the development, based on multivariate data analysis techniques and methods, of an aggregated indicator of social inclusion for the member countries of the European Union which, besides the traditional variables (GDP), also measures the factors related to civil and political rights, women’s rights or perception of the LGBT community.

Optimization techniques perform an important role in different domains of statistic. Examples of parameter estimation of different distributions, correlation analysis (parametric and nonparametric), regression analysis, optimal allocation of resources in partial research, exploration of response surfaces, design of experiments, efficiency tests, reliability theory, survival analysis are the most known methods of statistical analysis in which we find optimization techniques. The paper contains a synthetic presentation of the main statistical methods using classical optimization techniques, numerical optimization methods, linear and nonlinear programming, variational calculus techniques. Also, an example of applying the “simplex” algorithm in making a decision to invest an amount on the stock exchange, using a prediction model..

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Competitive businesses need to study the behavior of their current and potential customer base. Relevant data on the behavior can be obtained from online, where the purchase decisions are increasingly made and often based on product reviews, ratings and recommendations available in social media networks. The original data consists of 23486 customer reviews with ten variables/features of the reviewing customers, the products under review and the feedback to their reviews from online retail clothing business, and about half of the dataset is analyzed after cleaning the data. To find out, which features are the most important factors leading to a recommendation, the naïve Bayes and logistic regression methods are applied. Earlier research has shown that the sentiment of textual reviews and the given numerical ratings are key factors for the decision to recommend or not recommend products. The focus of this paper is to identify and rank-order the most relevant (numerical) factors affecting the review process leading to a recommendation. After applying the logistic regression classifier, we have found that rating, positive feedback count and age are statistically significant factors, in that order. The results support online retailers and manufacturers, as well, in adjusting their product portfolios and marketing efforts optimally to obtain recommendations for their products, reach potential customers and expose them to the given recommendations leading to positive purchase decisions. Further, the results indicate some future research opportunities.