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.
"synopsis" may belong to another edition of this title.
Francesca Gasperoni is currently employed in the pharmaceutical sector as trial statistician. Before moving to industry, she was a researcher at the MRC Biostatistics Unit (Cambridge, UK) and her research focussed on advanced statistical modelling for electronic heath records. During her PhD, obtained at Politecnico of Milan (Italy), she worked as teaching assistant for several courses of Probability and Statistics.
Francesca Ieva is Associate Professor of Statistics at the Department of Mathematics of the Polytechnic of Milan. She deals with statistical learning in the biomedical field and statistical modeling for complex data coming from the world of healthcare research. Since 2021 she is associate head of the Health Data Science Center of Human Technopole.
Anna Maria Paganoni is Full Professor of Statistics at the Department of Mathematics of the Polytechnic of Milan. She deals with statistical modeling and analysis of highly complex data with particular attention to the biomedical field and learning analytics. She is responsible for several competitively funded research projects. She is currently Coordinator of the Course of Studies in Mathematical Engineering, and delegate of the Rector for “Data Strategy”.
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.
"About this title" may belong to another edition of this title.
Seller: Books Puddle, New York, NY, U.S.A.
Condition: New. Seller Inventory # 26403938203
Seller: Majestic Books, Hounslow, United Kingdom
Condition: New. Seller Inventory # 409216068
Quantity: 1 available
Seller: GreatBookPrices, Columbia, MD, U.S.A.
Condition: New. Seller Inventory # 50236830-n
Seller: PBShop.store US, Wood Dale, IL, U.S.A.
PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000. Seller Inventory # GB-9783031866692
Seller: PBShop.store UK, Fairford, GLOS, United Kingdom
PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000. Seller Inventory # GB-9783031866692
Quantity: 1 available
Seller: Biblios, Frankfurt am main, HESSE, Germany
Condition: New. Seller Inventory # 18403938193
Quantity: 1 available
Seller: Grand Eagle Retail, Bensenville, IL, U.S.A.
Paperback. Condition: new. Paperback. 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9783031866692
Seller: GreatBookPrices, Columbia, MD, U.S.A.
Condition: As New. Unread book in perfect condition. Seller Inventory # 50236830
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Condition: New. Seller Inventory # 50236830-n
Quantity: 1 available
Seller: Revaluation Books, Exeter, United Kingdom
Paperback. Condition: Brand New. 315 pages. 9.26x6.11 inches. In Stock. Seller Inventory # __303186669X
Quantity: 1 available