Statistical foundations, reasoning and inference : for science and data science / Göran Kauermann, Helmut Küchenhoff, Christian Heumann.
Kauermann, Göran, author.; Küchenhoff, Helmut, author.; Heumann, Christian, 1962- author.
2021
QA276
Available Online

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Title
Statistical foundations, reasoning and inference : for science and data science / Göran Kauermann, Helmut Küchenhoff, Christian Heumann.
Author
Kauermann, Göran, author.
ISBN
9783030698270 (electronic bk.)
3030698270 (electronic bk.)
3030698262
9783030698263
DOI
https://doi.org/10.1007/978-3-030-69827-0
Publication Details
Cham, Switzerland : Springer, 2021.
Language
English
Description
1 online resource
Item Number
10.1007/978-3-030-69827-0 doi
Call Number
QA276
Dewey Decimal Classification
519.5
Summary
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' 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.
Bibliography, etc. Note
Includes bibliographical references and index.
Access Note
Access limited to authorized users.
Digital File Characteristics
text file
PDF
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed October 5, 2021).
Added Author
Küchenhoff, Helmut, author.
Heumann, Christian, 1962- author.
Series
Springer series in statistics, 2197-568X
Available in Other Form
Print version: 9783030698263
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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.

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