Statistical foundations, reasoning and inference: for science and data science

Contributor(s):

Material type: Text

TextSeries: Springer Series in Statistics (SSS)Publication details: Cham Springer 2021Description: xiii, 356 pISBN:

9783030698294

Subject(s):

DDC classification:

519.5 KAU

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’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 (https://link.springer.com/book/10.1007/978-3-030-69827-0)

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Holdings
Item type	Current library	Call number	Copy number	Status	Date due	Barcode
Book	Indian Institute of Management LRC General Stacks		1	Available		009151

Table of contents:
Front Matter
Pages i-xiii
Download chapter PDF
Introduction
Göran Kauermann, Helmut Küchenhoff, Christian Heumann
Pages 1-5
Background in Probability
Göran Kauermann, Helmut Küchenhoff, Christian Heumann
Pages 7-31
Parametric Statistical Models
Göran Kauermann, Helmut Küchenhoff, Christian Heumann
Pages 33-62
Maximum Likelihood Inference
Göran Kauermann, Helmut Küchenhoff, Christian Heumann
Pages 63-81
Bayesian Statistics
Göran Kauermann, Helmut Küchenhoff, Christian Heumann
Pages 83-112
Statistical Decisions
Göran Kauermann, Helmut Küchenhoff, Christian Heumann
Pages 113-153
Regression
Göran Kauermann, Helmut Küchenhoff, Christian Heumann
Pages 155-196
Bootstrapping
Göran Kauermann, Helmut Küchenhoff, Christian Heumann
Pages 197-229
Model Selection and Model Averaging
Göran Kauermann, Helmut Küchenhoff, Christian Heumann
Pages 231-255
Multivariate and Extreme Value Distributions
Göran Kauermann, Helmut Küchenhoff, Christian Heumann
Pages 257-281
Missing and Deficient Data
Göran Kauermann, Helmut Küchenhoff, Christian Heumann
Pages 283-320
Experiments and Causality
Göran Kauermann, Helmut Küchenhoff, Christian Heumann
Pages 321-346
Back Matter
Pages 347-356

[https://link.springer.com/book/10.1007/978-3-030-69827-0]

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

(https://link.springer.com/book/10.1007/978-3-030-69827-0)

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