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Statistical Foundations, Reasoning and Inference: For Science and Data Science (Springer Series in Statistics)

Statistical Foundations, Reasoning and Inference: For Science and Data Science (Springer Series in Statistics)

Statistical Foundations, Reasoning and Inference: For Science and Data Science
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Statistical Foundations, Reasoning and Inference: For Science and Data Science (Springer Series in Statistics)

by Kauermann, Göran

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Springer. Used - Good. Ships from UK in 48 hours or less (usually same day). Your purchase helps support Sri Lankan Children's Charity 'The Rainbow Centre'. Shows some signs of wear but in good overall condition. 100% money back guarantee. We are a world class secondhand bookstore based in Hertfordshire, United Kingdom and specialize in high quality textbooks across an enormous variety of subjects. We aim to provide a vast range of textbooks, rare and collectible books at a great price. Our donations to The Rainbow Centre have helped provide an education and a safe haven to hundreds of children who live in appalling conditions. We provide a 100% money back guarantee and are dedicated to providing our customers with the highest standards of service in the bookselling industry.
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Details

  • Title Statistical Foundations, Reasoning and Inference: For Science and Data Science (Springer Series in Statistics)
  • Author Kauermann, Göran
  • Condition Used - Good
  • Publisher Springer
  • Features Illustrated
  • Bookseller's Inventory # Z1-C-083-02904
  • ISBN 9783030698263

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Phatpocket Limited is a world class secondhand bookstore located in the Hertfordshire countryside in the United Kingdom. We specialize in textbooks across an enormous variety of subjects. We aim to provide a low cost source of high quality textbooks to the academic community. We also have a sizable collection of rare and collectible books.

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Books are usually shipped in 48 hours or less. All of our books have a 14 day no hassle money back guarantee unless stated otherwise in the book's description. Item must be returned in the exact same condition that it was received. Through our work with The Rainbow Centre and other Charity Partners, we have already given hundreds of young people in Sri Lanka and Africa the vital chance to get an education.

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Reader reviews for Statistical Foundations, Reasoning and Inference: For Science and Data Science (Springer Series in Statistics)

From the publisher

This textbook provides a comprehensive introduction to statistical principles, concepts and methods that are essential in modern statistics and data science. The topics covered include likelihood-based inference, Bayesian statistics, regression, statistical tests and the quantification of uncertainty. Moreover, the book addresses statistical ideas that are useful in modern data analytics, including bootstrapping, modeling of multivariate distributions, missing data analysis, causality as well as principles of experimental design. The textbook includes sufficient material for a two-semester course and is intended for master's students in data science, statistics and computer science with a rudimentary grasp of probability theory. It will also be useful for data science practitioners who want to strengthen their statistics skills.

From the rear cover

This textbook provides a comprehensive introduction to statistical principles, concepts and methods that are essential in modern statistics and data science. The topics covered include likelihood-based inference, Bayesian statistics, regression, statistical tests and the quantification of uncertainty. Moreover, the book addresses statistical ideas that are useful in modern data analytics, including bootstrapping, modeling of multivariate distributions, missing data analysis, causality as well as principles of experimental design. The textbook includes sufficient material for a two-semester course and is intended for master's students in data science, statistics and computer science with a rudimentary grasp of probability theory. It will also be useful for data science practitioners who want to strengthen their statistics skills.

About the author

Gran Kauermann is a Professor of Statistics at the Department of Statistics and Chair of the Elite Master's Program in Data Science at the LMU Munich, Germany. He is a recognized expert in applied statistics. He previously served as Editor-in-Chief of AStA Advances in Statistical Analysis, a journal of the German Statistical Society.

Helmut Kchenhoff is a Professor of Statistics at the Department of Statistics and Head of the Statistical Consulting Unit (StaBLab) at the LMU Munich, Germany. He has extensive experience in working on practical statistical projects in science and industry. His teaching focuses on practical work, where students engage in practical projects with real-world problems.

Christian Heumann is a Professor at the Department of Statistics, LMU Munich, Germany, where he teaches students in both the Bachelor's and Master's programs. His research interests include statistical modeling, computational statistics and methods for missing data, also in connection with causal inference. Recently, he has begun exploring statistical methods in natural language processing.


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