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Mathematical statistics --- Quantile regression --- Regression Analysis

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The Covid emergency has forced universities around the world to transfer teaching activities online. Even if online teaching has made it possible to carry out the planned teaching activities, it is necessary, in retrospect, to evaluate the impact that this teaching method has had on the different types of students, in terms of preparation, characteristics and social background. In this framework, the presents paper aims to evaluate if distance learning can be considered socially less useful because it increases the divide between the advantaged and disadvantaged students. The study is based on the analysis of data collected at the University of Naples Federico II in June 2020. More than 19 thousand students took part in the survey, carried out to monitor distance learning activities. The aim of this work is to analyse whether and how much the distance learning activities has had an impact on the students' families both in terms of the organisation of the spaces and daily rhythms and from an economic point of view, having required additional expenses. This objective will be achieved through the use of a factorial method that will provide a composite indicator measuring the family impact of distance learning. We will then try to explain if the family impact takes different forms and intensity depending on the students' characteristics, the availability of computer equipment and the type of teaching used. Quantile regression will allow to differentiate the study of effects for different levels of family impact. Finally, it will also be evaluated whether the experience lived in terms of the family impact of the distance learning, conditions the judgement on the preferred teaching method for the future, totally online, oriented towards a complete return to face-to-face teaching or a mixed solution that takes advantage of the experience lived.

Distance education.

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The contributions gathered in this book focus on modern methods for statistical learning and modeling in data analysis and present a series of engaging real-world applications. The book covers numerous research topics, ranging from statistical inference and modeling to clustering and factorial methods, from directional data analysis to time series analysis and small area estimation. The applications reflect new analyses in a variety of fields, including medicine, finance, engineering, marketing and cyber risk. The book gathers selected and peer-reviewed contributions presented at the 12th Scientific Meeting of the Classification and Data Analysis Group of the Italian Statistical Society (CLADAG 2019), held in Cassino, Italy, on September 11-13, 2019. CLADAG promotes advanced methodological research in multivariate statistics with a special focus on data analysis and classification, and supports the exchange and dissemination of ideas, methodological concepts, numerical methods, algorithms, and computational and applied results. This book, true to CLADAG's goals, is intended for researchers and practitioners who are interested in the latest developments and applications in the field of data analysis and classification.

Statistical science --- Mathematical control systems --- Mathematical statistics --- Programming --- Information systems --- Artificial intelligence. Robotics. Simulation. Graphics --- Computer. Automation --- datamining --- informatica --- statistiek --- programmeren (informatica) --- informatietechnologie --- data acquisition --- informatietheorie --- statistisch onderzoek