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This book was created with the goal of helping students transition from the theoretical and methodological concepts of statistical inference to their implementation on a computer. The first part of the book is primarily focused on exercises to be solved with pen and paper, so that students can apply knowledge derived from lemmas and theorems; while the second part consists of labs, which involve both the manual implementation of algorithms and the learning of built-in tools for efficient analysis of datasets derived from real-world problems. To optimize the understanding of the topics…mehr

Produktbeschreibung
This book was created with the goal of helping students transition from the theoretical and methodological concepts of statistical inference to their implementation on a computer. The first part of the book is primarily focused on exercises to be solved with pen and paper, so that students can apply knowledge derived from lemmas and theorems; while the second part consists of labs, which involve both the manual implementation of algorithms and the learning of built-in tools for efficient analysis of datasets derived from real-world problems. To optimize the understanding of the topics developed and to guide the reader through their studies, the book is organized into chapters, each of which includes an introductory section that reviews the theoretical foundations of statistical inference, followed by a second part with exercises, each accompanied by a comprehensive solution on paper and, when appropriate, using software. This book is aimed at undergraduate students in Statistics,Mathematics, Engineering, and for graduate-level courses in Data Science.
Autorenporträt
Francesca Gasperoni è Ricercatrice postdoc presso l'unità di Biostatistica dell'Università di Cambridge. La sua area di ricerca è la statistica applicata alla clinica, con particolare attenzione a modelli per l'analisi di sopravvivenza, di eventi competitivi e sequenziali. Durante il Dottorato di Ricerca, conseguito nel 2019 al Politecnico di Milano, è stata assistente in diversi corsi di Probabilità e Statistica. Francesca Ieva è Professore Associato di Statistica presso il Dipartimento di Matematica del Politecnico di Milano. Si occupa di apprendimento statistico in ambito biomedico e di modellazione statistica per dati complessi e non strutturati provenienti dal mondo della ricerca in ambito sanitario. Anna Maria Paganoni è Professore Ordinario di Statistica presso il Dipartimento di Matematica del Politecnico di Milano. Si occupa di modellazione statistica e analisi di dati ad alta complessità con particolare attenzione all'ambito biomedico e al learning analytics. E' responsabile di diversi progetti di ricerca a finanziamento competitivo. Dal 2019 è Coordinatore del Corso di Studi in Ingengeria Matematica.