| Dewey Class |
519.5 |
| Title |
Statistical Foundations, Reasoning and Inference ([EBook] :) : For Science and Data Science / / by Göran Kauermann, Helmut Küchenhoff, Christian Heumann. |
| Author |
Kauermann, Göran |
| Added Personal Name |
Küchenhoff, Helmut |
| Heumann, Christian |
| Other name(s) |
SpringerLink (Online service) |
| Edition statement |
1st ed. 2021. |
| Publication |
Cham : : Springer International Publishing : : Imprint: Springer, , 2021. |
| Physical Details |
XIII, 356 p. 87 illus., 10 illus. in color. : online resource. |
| Series |
Springer Series in Statistics 2197-568X |
| ISBN |
9783030698270 |
| Summary Note |
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.: |
| Contents note |
Introduction -- Background in Probability -- Parametric Statistical Models -- Maximum Likelihood Inference -- Bayesian Statistics -- Statistical Decisions -- Regression -- Bootstrapping -- Model Selection and Model Averaging -- Multivariate and Extreme Value Distributions -- Missing and Deficient Data -- Experiments and Causality. |
| Mode of acces to digital resource |
Mode of access: World Wide Web. System requirements: Internet Explorer 6.0 (or higher) or Firefox 2.0 (or higher). Available as searchable text in PDF format. |
| System details note |
Online access to this digital book is restricted to subscription institutions through IP address (only for SISSA internal users). |
| Internet Site |
https://doi.org/10.1007/978-3-030-69827-0 |
| Links to Related Works |
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