Statistical Foundations, Reasoning and Inference : For Science and Data Science /

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 uncertaint...

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Bibliographic Details
Main Authors:	Kauermann, Göran (Author), Küchenhoff, Helmut (Author), Heumann, Christian (Author)
Corporate Author:	SpringerLink (Online service)
Format:	eBook
Language:	English
Published:	Cham : Springer International Publishing : Imprint: Springer, 2021.
Edition:	1st ed. 2021.
Series:	Springer Series in Statistics,
Subjects:	Statistics . Data structures (Computer science). Artificial intelligence. Data mining. Statistical Theory and Methods. Data Structures and Information Theory. Artificial Intelligence. Data Mining and Knowledge Discovery. Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. Electronic books.
Online Access:	Connect to the full text of this electronic book

Table of Contents:

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.