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Springer Statistical Foundations, Reasoning and Inference Book

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Description

Master the essential principles of modern statistics and data science with this comprehensive textbook from Springer. Part of the Springer Series in Statistics, this book provides a deep dive into likelihood-based inference, Bayesian statistics, and regression models. It is designed to equip readers with the tools needed for modern data analytics, covering topics such as bootstrapping, multivariate distributions, and missing data analysis. Beyond core concepts, the text explores causality and principles of experimental design to build a strong foundation in reasoning and inference. This resource is ideal for those studying science and data science, offering enough material to support a two-semester course. Whether you are studying statistical tests or the quantification of uncertainty, this book serves as a fundamental guide for navigating complex data landscapes.

Key Features

This Springer textbook covers essential statistical principles including likelihood-based inference and Bayesian statistics for science and data science.

The content includes practical methods for modern data analytics such as bootstrapping, modeling multivariate distributions, and missing data analysis.

Learn to apply statistical tests and the quantification of uncertainty to improve accuracy in data-driven research.

The material addresses advanced topics like causality and principles of experimental design to strengthen statistical reasoning.

This book provides sufficient depth and material to serve as a complete resource for a two-semester course.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
02 October 2022
Listed Since
04 September 2022

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