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Statistical Rethinking

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Statistical Rethinking

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Author : Richard McElreath
language : en
Publisher: CRC Press
Release Date : 2018-01-03

Statistical Rethinking written by Richard McElreath and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-01-03 with Mathematics categories.

Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds readers’ knowledge of and confidence in statistical modeling. Reflecting the need for even minor programming in today’s model-based statistics, the book pushes readers to perform step-by-step calculations that are usually automated. This unique computational approach ensures that readers understand enough of the details to make reasonable choices and interpretations in their own modeling work. The text presents generalized linear multilevel models from a Bayesian perspective, relying on a simple logical interpretation of Bayesian probability and maximum entropy. It covers from the basics of regression to multilevel models. The author also discusses measurement error, missing data, and Gaussian process models for spatial and network autocorrelation. By using complete R code examples throughout, this book provides a practical foundation for performing statistical inference. Designed for both PhD students and seasoned professionals in the natural and social sciences, it prepares them for more advanced or specialized statistical modeling. Web Resource The book is accompanied by an R package (rethinking) that is available on the author’s website and GitHub. The two core functions (map and map2stan) of this package allow a variety of statistical models to be constructed from standard model formulas.

Statistical Rethinking

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Author : Richard McElreath
language : en
Publisher: CRC Press
Release Date : 2018-01-03

Statistical Modeling With R

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Author : Pablo Inchausti
language : en
Publisher: Oxford University Press
Release Date : 2023-01-16

Statistical Modeling With R written by Pablo Inchausti and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-16 with Science categories.

To date, statistics has tended to be neatly divided into two theoretical approaches or frameworks: frequentist (or classical) and Bayesian. Scientists typically choose the statistical framework to analyse their data depending on the nature and complexity of the problem, and based on their personal views and prior training on probability and uncertainty. Although textbooks and courses should reflect and anticipate this dual reality, they rarely do so. This accessible textbook explains, discusses, and applies both the frequentist and Bayesian theoretical frameworks to fit the different types of statistical models that allow an analysis of the types of data most commonly gathered by life scientists. It presents the material in an informal, approachable, and progressive manner suitable for readers with only a basic knowledge of calculus and statistics. Statistical Modeling with R is aimed at senior undergraduate and graduate students, professional researchers, and practitioners throughout the life sciences, seeking to strengthen their understanding of quantitative methods and to apply them successfully to real world scenarios, whether in the fields of ecology, evolution, environmental studies, or computational biology.

Statistical Rethinking

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Author : Richard McElreath
language : en
Publisher:
Release Date : 2018

Statistical Rethinking written by Richard McElreath and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with R (Computer program language) categories.

Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds readers' knowledge of and confidence in statistical modeling. Reflecting the need for even minor programming in today's model-based statistics, the book pushes readers to perform step-by-step calculations that are usually automated. This unique computational approach ensures that readers understand enough of the details to make reasonable choices and interpretations in their own modeling work. The text presents generalized linear multilevel models from a Bayesian perspective, relying on a simple logical interpretation of Bayesian probability and maximum entropy. It covers from the basics of regression to multilevel models. The author also discusses measurement error, missing data, and Gaussian process models for spatial and network autocorrelation. By using complete R code examples throughout, this book provides a practical foundation for performing statistical inference. Designed for both PhD students and seasoned professionals in the natural and social sciences, it prepares them for more advanced or specialized statistical modeling. Web Resource The book is accompanied by an R package (rethinking) that is available on the author's website and GitHub. The two core functions (map and map2stan) of this package allow a variety of statistical models to be constructed from standard model formulas.

Statistics By Simulation

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Author : Carsten F. Dormann
language : en
Publisher: Princeton University Press
Release Date : 2025-06-03

Statistics By Simulation written by Carsten F. Dormann and has been published by Princeton University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-03 with Science categories.

An accessible guide to understanding statistics using simulations, with examples from a range of scientific disciplines Real-world challenges such as small sample sizes, skewed distributions of data, biased sampling designs, and more predictors than data points are pushing the limits of classical statistical analysis. This textbook provides a new tool for the statistical toolkit: data simulations. It shows that using simulation and data-generating models is an excellent way to validate statistical reasoning and to augment study design and statistical analysis with planning and visualization. Although data simulations are not new to professional statisticians, Statistics by Simulation makes the approach accessible to a broader audience, with examples from many fields. It introduces the reasoning behind data simulation and then shows how to apply it in planning experiments or observational studies, developing analytical workflows, deploying model diagnostics, and developing new indices and statistical methods. • Covers all steps of statistical practice, from planning projects to post-hoc analysis and model checking • Provides examples from disciplines including sociology, psychology, ecology, economics, physics, and medicine • Includes R code for all examples, with data and code freely available online • Offers bullet-point outlines and summaries of each chapter • Minimizes the use of jargon and requires only basic statistical background and skills

Statistical Computing With R Second Edition

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Author : Maria L. Rizzo
language : en
Publisher: CRC Press
Release Date : 2019-02-21

Statistical Computing With R Second Edition written by Maria L. Rizzo and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-02-21 with Business & Economics categories.

Computational statistics and statistical computing are two areas that employ computational, graphical, and numerical approaches to solve statistical problems, making the versatile R language an ideal computing environment for these fields. This second edition continues to encompass the traditional core material of computational statistics, with an

Experimentology

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Author : Michael C. Frank
language : en
Publisher: MIT Press
Release Date : 2025-07-01

Experimentology written by Michael C. Frank and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-01 with Psychology categories.

An engaging research methods text integrating a classic approach to conducting experiments in psychology with open science practices and values. How does a researcher run a high-quality psychology experiment? What time-tested methods should be used, and how can more robust and accurate results be achieved? A dynamic collaboration between groundbreaking cognitive scientist Michael Frank and a diverse cohort of researchers innovating in the field—Mika Braginsky, Julie Cachia, Nicholas Coles, Tom Hardwicke, Robert Hawkins, Maya Mathur, and Rondeline Williams—Experimentology introduces the art of the modern psychological experiment with an emphasis on open science values of accessibility and transparency. Experimentology follows the timeline of an experiment, with sections covering basic foundations, planning, execution, data-gathering and analysis, and reporting. Narrative examples from a range of subdisciplines, including cognitive, developmental, and social psychology, model each component and account for the pitfalls that can undermine the reliability, validity, and replicability of results. Through an embrace of open science strategies such as data sharing and preregistration, Experimentology shows how the challenges of the replication crisis can be met constructively and collaboratively. Written for a global audience, Experimentology updates a classic research methods textbook with a new focus on ethics and the benefits of open science.

Introduction To Statistical Methods For Financial Models

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Author : Thomas A Severini
language : en
Publisher: CRC Press
Release Date : 2017-07-06

Introduction To Statistical Methods For Financial Models written by Thomas A Severini and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-07-06 with Business & Economics categories.

This book provides an introduction to the use of statistical concepts and methods to model and analyze financial data. The ten chapters of the book fall naturally into three sections. Chapters 1 to 3 cover some basic concepts of finance, focusing on the properties of returns on an asset. Chapters 4 through 6 cover aspects of portfolio theory and the methods of estimation needed to implement that theory. The remainder of the book, Chapters 7 through 10, discusses several models for financial data, along with the implications of those models for portfolio theory and for understanding the properties of return data. The audience for the book is students majoring in Statistics and Economics as well as in quantitative fields such as Mathematics and Engineering. Readers are assumed to have some background in statistical methods along with courses in multivariate calculus and linear algebra.

Bayesian Statistics

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Author : Thomas J. Faulkenberry
language : en
Publisher: Taylor & Francis
Release Date : 2025-04-30

Bayesian Statistics written by Thomas J. Faulkenberry and has been published by Taylor & Francis this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-30 with Mathematics categories.

Bayesian Statistics: The Basics provides a comprehensive yet accessible introduction to Bayesian statistics, specifically tailored for any researcher with an interest in statistical methods. It covers the theoretical foundations of Bayesian inference, contrasting it with classical statistical methods like null hypothesis significance testing. The book emphasizes key concepts such as prior and posterior distributions, Bayes’ theorem, and the Bayes factor, making them understandable even for readers with minimal mathematical backgrounds. Methodologically, the book offers practical, step-by-step guides on how to conduct Bayesian analyses using the free software package JASP. Each chapter focuses on applying Bayesian methods to common research designs with real-world data. Readers will benefit from the clear examples, visualizations, and JASP screenshots that ensure the learning experience is interactive and easy to follow. Full of practical content, the book emphasizes the advantages of Bayesian model comparison over traditional approaches, especially in quantifying evidence for competing hypotheses. Readers will also learn how to perform sensitivity analyses to assess the impact of different prior assumptions on their results. By the end of the book, readers will get both the theoretical understanding and practical skills to implement Bayesian methods in their own research, making it an invaluable resource for both novice and experienced researchers studying Bayesian statistics.

Statistics For Linguistics With R

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Author : Stefan Th. Gries
language : en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date : 2021-05-10

Statistics For Linguistics With R written by Stefan Th. Gries and has been published by Walter de Gruyter GmbH & Co KG this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-05-10 with Language Arts & Disciplines categories.

This is the third, newly revised and extended edition of this successful book (that has already been translated into three languages). Like the previous editions, it is entirely based on the programming language and environment R and is still thoroughly hands-on (with thousands of lines of heavily annotated code for all computations and plots). However, this edition has been updated based on many workshops/bootcamps taught by the author all over the world for the past few years: This edition has been didactically streamlined with regard to its exposition, it adds two new chapters – one on mixed-effects modeling, one on classification and regression trees as well as random forests – plus it features new discussion of curvature, orthogonal and other contrasts, interactions, collinearity, the effects and emmeans packages, autocorrelation/runs, some more bits on programming, writing statistical functions, and simulations, and many practical tips based on 10 years of teaching with these materials.

Statistical Rethinking

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