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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, Küchenhoff, Helmut (Author), Heumann, Christian (Author)
Format: eBook
Language:English
Published: Cham Springer International Publishing 2021, 2021
Edition:1st ed. 2021
Series:Springer Series in Statistics
Subjects:
Data Structures And Information Theory
Statistical Theory And Methods
Statistics 
Data Mining
Artificial Intelligence
Data Mining And Knowledge Discovery
Statistics For Engineering, Physics, Computer Science, Chemistry And Earth Sciences
Data Structures (computer Science)
Online Access:
Collection: Springer eBooks 2005- - Collection details see MPG.ReNa
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

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