Books [Benjamin Yakir] Linear Regression Using R: An Introduction to Data Modeling
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This book presents one of the fundamental data modeling techniques in an informal tutorial style. Learn how to predict system outputs from measured data using a detailed step-by-step process to develop, train, and test reliable regression models. Key modeling and programming concepts are intuitively described using the R programming language. All of the necessary resources are freely available online.
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Books [Benjamin Yakir] Linear Regression Using R: An Introduction to Data Modeling 2 edition
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This book presents one of the fundamental data modeling techniques in an informal tutorial style. Learn how to predict system outputs from measured data using a detailed step-by-step process to develop, train, and test reliable regression models. Key modeling and programming concepts are intuitively described using the R programming language. All of the necessary resources are freely available online.
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Books [Benjamin Yakir] Introduction to Statistical Thinking (With R, Without Calculus)
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This is an introduction to statistics, with R, without calculus. The target audience for this book is college students who are required to learn statistics, students with little background in mathematics and often no motivation to learn more.
It shows you how to derive actionable conclusions from data analysis, solve real problems, and improve real processes. Here, you'll discover how to implement statistical thinking and methodology in your work to improve business performance.
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Books [Silvia Bozza, Franco Taroni, Alex Biedermann] Bayes Factors for Forensic Decision Analyses with R
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This book provides a self-contained introduction to computational Bayesian statistics using R programming language. With its primary focus on Bayes factors supported by data sets, this book features an operational perspective, practical relevance, and applicability―keeping theoretical and philosophical justifications limited.
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Books [Alicia A. Johnson, Miles Q. Ott, Mine Dogucu] Bayes Rules! An Introduction to Applied Bayesian Modeling
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An engaging, sophisticated, and fun introduction to the field of Bayesian statistics, Bayes Rules!: An Introduction to Applied Bayesian Modeling brings the power of modern Bayesian thinking, modeling, and computing to a broad audience. In particular, the book is an ideal resource for advanced undergraduate statistics students and practitioners with comparable experience. the book assumes that readers are familiar with the content covered in a typical undergraduate-level introductory statistics course. Readers will also, ideally, have some experience with undergraduate-level probability, calculus, and the R statistical software. Readers...
Books [Merlise Clyde, Mine Çetinkaya-Rundel, Colin Rundel, David Banks, Christine Chai, Lizzy Huang] An Introduction to Bayesian Thinking
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The goal of this book is to provide an introduction to Bayesian Inference in decision making without requiring calculus. It may be used on its own as an open-access introduction to Bayesian inference using R Programming Language for anyone interested in learning about Bayesian statistics.
The book focuses on Bayesian methods applied routinely in practice including multiple linear regression, mixed effects models and generalized linear models (GLM). The authors include many examples with complete R code and comparisons with analogous frequentist procedures.
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Books [David Dalpiaz] Applied Statistics with R
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This book provides an integrated treatment of statistical inference techniques in data science using the R Statistical Software. It provides a much-needed, easy-to-follow introduction to statistics and the R programming language.
It introduces foundational statistics concepts with beginner-friendly R programming in an exploration of the world's tricky problems faced by the "R Team" characters
The book provides readers with the conceptual foundation to use applied statistical methods in everyday research. Each statistical method is developed within the context of practical, real-world examples and is supported by carefully developed...
Books [Eva Bartz, Thomas Bartz-Beielstein, Martin Zaefferer, Olaf Mersmann] Hyperparameter Tuning for Machine and Deep Learning with R: A Practical Guide
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This open access book provides a wealth of hands-on examples that illustrate how hyperparameter tuning can be applied in practice and gives deep insights into the working mechanisms of machine learning (ML) and deep learning (DL) methods. The aim of the book is to equip readers with the ability to achieve better results with significantly less time, costs, effort and resources using the methods described here. The case studies presented in this book can be run on a regular desktop or notebook computer. No high-performance computing facilities are required.
The idea for the book originated in a study conducted by Bartz & Bartz GmbH for...
Books [Vijay Kotu, Bala Deshpande] Data Science: Concepts and Practice 2 edition
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Learn the basics of Data Science through an easy to understand conceptual framework and immediately practice using RapidMiner platform. Whether you are brand new to data science or working on your tenth project, this book will show you how to analyze data, uncover hidden patterns and relationships to aid important decisions and predictions.
Data Science has become an essential tool to extract value from data for any organization that collects, stores and processes data as part of its operations. This book is ideal for business users, data analysts, business analysts, engineers, and analytics professionals and for anyone who works with...
Books [Rafael A. Irizarry] Introduction to Data Science: Data Analysis and Prediction Algorithms with R
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Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation.
This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with...
Books [Rob J. Hyndman, George Athanasopoulos] Forecasting: Principles and Practice 2 edition
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Forecasting is required in many situations. Deciding whether to build another power generation plant in the next five years requires forecasts of future demand. Scheduling staff in a call centre next week requires forecasts of call volumes. Stocking an inventory requires forecasts of stock requirements. Telecommunication routing requires traffic forecasts a few minutes ahead. Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning. This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to...
Books [Rob J. Hyndman, George Athanasopoulos] Forecasting: Principles and Practice
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Forecasting is required in many situations. Deciding whether to build another power generation plant in the next five years requires forecasts of future demand. Scheduling staff in a call centre next week requires forecasts of call volumes. Stocking an inventory requires forecasts of stock requirements. Telecommunication routing requires traffic forecasts a few minutes ahead. Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning. This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to...
Books [Guy Lansley, James Cheshire] An Introduction to Spatial Data Analysis and Visualisation in R
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Books [Edzer Pebesma, Roger Bivand] Spatial Data Science: With Applications in R
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Spatial Data Science introduces fundamental aspects of spatial data that every data scientist should know before they start working with spatial data. These aspects include how geometries are represented, coordinate reference systems (projections, datums), the fact that the Earth is round and its consequences for analysis, and how attributes of geometries can relate to geometries. In the second part of the book, these concepts are illustrated with data science examples using the R language. In the third part, statistical modelling approaches are demonstrated using real world data examples. After reading this book, the reader will be...
Books [Michael C. Wimberly] Geographic Data Science with R: Visualizing and Analyzing Environmental Change
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The burgeoning field of data science has provided a wealth of techniques for analysing large and complex geospatial datasets, including descriptive, explanatory, and predictive analytics. However, applying these methods is just one part of the overall process of geographic data science. Other critical steps include screening for suspect data values, handling missing data, harmonizing data from multiple sources, summarizing the data, and visualizing data and analysis results. Although there are many books available on statistical and machine learning methods, few encompass the broader topic of scientific workflows for geospatial data...
Books [Robin Lovelace, Jakub Nowosad, Jannes Muenchow] Geocomputation with R
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Geocomputation with R is for people who want to analyze, visualize and model geographic data with open source software. It is based on R, a statistical programming language that has powerful data processing, visualization, and geospatial capabilities. The book equips you with the knowledge and skills to tackle a wide range of issues manifested in geographic data, including those with scientific, societal, and environmental implications. This book will interest people from many backgrounds, especially Geographic Information Systems (GIS) users interested in applying their domain-specific knowledge in a powerful open source language for...
Books [Yihui Xie] bookdown: Authoring Books and Technical Documents with R Markdown
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bookdown: Authoring Books and Technical Documents with R Markdown presents a much easier way to write books and technical publications than traditional tools such as LaTeX and Word. The bookdown package inherits the simplicity of syntax and flexibility for data analysis from R Markdown, and extends R Markdown for technical writing, so that you can make better use of document elements such as figures, tables, equations, theorems, citations, and references. Similar to LaTeX, you can number and cross-reference these elements with bookdown. Your document can even include live examples so readers can interact with them while reading the...
Books [Yihui Xie, Amber Thomas, Alison Presmanes Hill] blogdown: Creating Websites with R Markdown
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blogdown: Creating Websites with R Markdown provides a practical guide for creating websites using the blogdown package in R. In this book, we show you how to use dynamic R Markdown documents to build static websites featuring R code (or other programming languages) with automatically rendered output such as graphics, tables, analysis results, and HTML widgets. The blogdown package is also suitable for technical writing with elements such as citations, footnotes, and LaTeX math. This makes blogdown an ideal platform for any website designed to communicate information about data science, data analysis, data visualization, or R...
Books [Yihui Xie, J. J. Allaire, Garrett Grolemund] R Markdown: The Definitive Guide
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This is the first official book authored by the core R Markdown developers that provides a comprehensive and accurate reference to the R Markdown ecosystem.
With R Markdown, you can easily create reproducible data analysis reports, presentations, dashboards, interactive applications, books, dissertations, websites, and journal articles, while enjoying the simplicity of Markdown and the great power of R and other languages.
About the Authors
- Yihui Xie is a software engineer at RStudio.
- J.J. Allaire is the founder of RStudio and the creator of the RStudio IDE.
- Garrett Grolemund is the co-author of R for Data Science...
Books [Chester Ismay, Albert Y. Kim] Statistical Inference via Data Science: A ModernDive into R and the Tidyverse
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Statistical Inference via Data Science: A ModernDive into R and the Tidyverse provides a pathway for learning about statistical inference using data science tools widely used in industry, academia, and government. It introduces the tidyverse suite of R packages, including the ggplot2 package for data visualization, and the dplyr package for data wrangling. After equipping readers with just enough of these data science tools to perform effective exploratory data analyses, the book covers traditional introductory statistics topics like confidence intervals, hypothesis testing, and multiple regression modeling, while focusing on...
Books [Norman Matloff] The Art of R Programming: A Tour of Statistical Software Design
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R is the world's most popular language for developing statistical software: Archaeologists use it to track the spread of ancient civilizations, drug companies use it to discover which medications are safe and effective, and actuaries use it to assess financial risks and keep economies running smoothly.
The Art of R Programming takes you on a guided tour of software development with R, from basic types and data structures to advanced topics like closures, recursion, and anonymous functions. No statistical knowledge is required, and your programming skills can range from hobbyist to pro.
Along the way, you'll learn about functional and...
Books [Emmanuel Paradis] R for Beginners
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Books [Emmanuel Paradis] A Step-by-Step R Tutorial: An Introduction into R Applications and Programming
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A Step-by-Step Tutorial in R has a two-fold aim: to learn the basics of R and to acquire basic skills for programming efficiently in R. Emphasis is on converting ideas about analysing data into useful R programs. It is stressed throughout that programming starts first by getting a clear understanding of the problem. Once the problem is well formulated the next phase is to write step-by-step code for execution by the R evaluator.
Although A Step-by-Step Tutorial in R is primarily intended as a course directed by an instructor, it can also be used with a little more effort as a self-teaching option. The first 11 chapters form the core...
Books [Patrick Burns] The R Inferno
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An essential guide to the trouble spots and oddities of R. In spite of the quirks exposed here, R is the best computing environment for most data analysis tasks. R is free, open-source, and has thousands of contributed packages. It is used in such diverse fields as ecology, finance, genomics and music. If you are using spreadsheets to understand data, switch to R. You will have safer - and ultimately, more convenient - computations.
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Books [Sasha D. Hafner] The Book of R: A First Course in Programming and Statistics
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The Book of R is a comprehensive, beginner-friendly guide to R, the world’s most popular programming language for statistical analysis. Even if you have no programming experience and little more than a grounding in the basics of mathematics, you’ll find everything you need to begin using R effectively for statistical analysis.
You’ll start with the basics, like how to handle data and write simple programs, before moving on to more advanced topics, like producing statistical summaries of your data and performing statistical tests and modeling. You’ll even learn how to create impressive data visualizations with R’s basic graphics tools...
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