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Statistical foundations, reasoning a...

Kauermann, Goran.

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Statistical foundations, reasoning and inference = for science and data science /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Statistical foundations, reasoning and inference/ by Goran Kauermann, Helmut Kuchenhoff, Christian Heumann.
Reminder of title:	for science and data science /
Author:	Kauermann, Goran.
other author:	Kuchenhoff, Helmut.
Published:	Cham :Springer International Publishing : : 2021.,
Description:	xiii, 356 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Mathematical statistics. -
Online resource:	https://doi.org/10.1007/978-3-030-69827-0
ISBN:	9783030698270

Statistical foundations, reasoning and inference = for science and data science /
Kauermann, Goran.

Statistical foundations, reasoning and inferencefor science and data science /[electronic resource] :by Goran Kauermann, Helmut Kuchenhoff, Christian Heumann. - Cham :Springer International Publishing :2021. - xiii, 356 p. :ill., digital ;24 cm. - Springer series in statistics,2197-568X. - Springer series in statistics..

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.

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.

ISBN: 9783030698270

Standard No.: 10.1007/978-3-030-69827-0doiSubjects--Topical Terms:

516858
Mathematical statistics.

LC Class. No.: QA276

Dewey Class. No.: 519.5

Statistical foundations, reasoning and inference = for science and data science /
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245 1 0 $a Statistical foundations, reasoning and inference $h [electronic resource] : $b for science and data science / $c by Goran Kauermann, Helmut Kuchenhoff, Christian Heumann.
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490 1 $a Springer series in statistics, $x 2197-568X
505 0 $a 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.
520 $a 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.
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