Statistical Foundations, Reasoning and Inference
For Science and Data Science
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Beschreibung
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.
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.
Introduces statistics and data science students to classical and modern statistical concepts Features detailed derivations and explanations of complex statistical methods Includes statistical tools for applied data science, e.g. for missing data or causality
Autor*in
Göran Kauermann
Themen in »Statistical Foundations, Reasoning and Inference«
statistical reasoning statistical inference data science data analytics statistics for data science statistical foundations likelihood-based inference Bayesian statistics statistical tests uncertainty quantification bootstrapping missing data causality multivariate distributions