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...
Main Authors: | , , |
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Format: | eBook |
Language: | English |
Published: |
Cham
Springer International Publishing
2021, 2021
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Edition: | 1st ed. 2021 |
Series: | Springer Series in Statistics
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Subjects: | |
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