Books [Brian S. Everitt, Torsten Hothorn] A Handbook of Statistical Analyses Using R
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Doing for R what Everitt's other Handbooks have done for S-PLUS, STATA, SPSS, and SAS, A Handbook of Statistical Analyses Using R presents straightforward, self-contained descriptions of how to perform a variety of statistical analyses in the R environment.
From simple inference to recursive partitioning and cluster analysis, eminent experts Everitt and Hothorn lead you methodically through the steps, commands, and interpretation of the results, addressing theory and statistical background only when useful or necessary.
They begin with an introduction to R, discussing the syntax, general operators, and basic data manipulation while...
Books [Robert Gentleman, Kurt Hornik, Giovanni Parmigiani] Use R!
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Books [William N Venables, David M Smith] An Introduction to R: A Programming Environment for Data Analysis and Graphics
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This tutorial manual provides a comprehensive introduction to R, a software package for statistical computing and graphics.
R supports a wide range of statistical techniques and is easily extensible via user-defined functions. One of R's strengths is the ease with which publication-quality plots can be produced in a wide variety of formats.
This is a printed edition of the tutorial documentation from the R distribution, with additional examples, notes and corrections. It is based on R version 2.9.0, released April 2009. R is free software, distributed under the terms of the GNU General Public License (GPL). It can be used with...
Books [Dr. Rob Kabacoff] R in Action: Data Analysis and Graphics with R
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Summary
R in Action, Second Edition presents both the R language and the examples that make it so useful for business developers. Focusing on practical solutions, the book offers a crash course in statistics and covers elegant methods for dealing with messy and incomplete data that are difficult to analyze using traditional methods. You'll also master R's extensive graphical capabilities for exploring and presenting data visually. And this expanded second edition includes new chapters on time series analysis, cluster analysis, and classification methodologies, including decision trees, random forests, and support vector machines...
Books [John Maindonald, John Braun] Data Analysis And Graphics Using R
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Books [Barton Poulson] R Succinctly
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Begin developing your mastery of the powerful R programming language. Become comfortable with the R environment and learn how to find ways for R to fulfill your data needs. With author Barton Poulson as your guide, you will soon be making use of this flexible, unparalleled tool. Unprecedented control of your data is within your grasp!
Analyzing your data is vital for success. Begin mastering statistical analysis with R Succinctly.
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Books [G. Jay Kerns] Introduction to Probability and Statistics Using R
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Books [Vincent Zoonekynd, Jose Gama] Statistics with R
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This book provides an elementary-level introduction to R, targeting both non-statistician scientists in various fields and students of statistics.
The main mode of presentation is via code examples with liberal commenting of the code and the output, from the computational as well as the statistical viewpoint. Brief sections introduce the statistical methods before they are used. A supplementary R package can be downloaded and contains the data sets.
All examples are directly runnable and all graphics in the text are generated from the examples. The statistical methodology covered includes statistical standard distributions, one- and...
Books [Gianluca Bontempi] Statistical Foundations of Machine Learning: The Handbook
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We are in the era of big data. There are essentially two reasons why people gather increasing volumes of data: first, they think some valuable assets are implicitly coded within them, and second computer technology enables effective data storage at reduced costs.
The procedure for finding useful patterns in data is known by different names in different communities but more and more, the set of computational techniques and tools to support the modelling of large amount of data is grouped under the label of machine learning.
This book aims to present the statistical foundations of machine learning intended as the discipline which deals...
Books [Daniel Navarro] Learning Statistics with R
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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.
This book 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.
At its core, this is an introductory statistics textbook pitched...
Books [Cosma Rohilla Shalizi] Advanced Data Analysis from an Elementary Point of View
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This is a textbook on data analysis methods, intended for advance undergraduate students who have already taken classes in probability, mathematical statistics, and linear regression. It began as the lecture notes for 36-402 at Carnegie Mellon University.
Every subject covered here can be profitably studied using vastly more sophisticated techniques; that's why this is advanced data analysis from an elementary point of view.
The book also presumes that you can read and write simple functions in R. If you are lacking in any of these areas, this book is not really for you, at least not now
About the Authors:
- Cosma Rohilla...
Books [Roger D. Peng] Exploratory Data Analysis with R
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Data science has taken the world by storm. Every field of study and area of business has been affected as people increasingly realize the value of the incredible quantities of data being generated. But to extract value from those data, one needs to be trained in the proper data science skills. The R programming language has become the de facto programming language for data science. Its flexibility, power, sophistication, and expressiveness have made it an invaluable tool for data scientists around the world. This book is about the fundamentals of R programming. You will get started with the basics of the language, learn how to...
Books [Julian Faraway] Linear Models with R Second Edition
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A Hands-On Way to Learning Data Analysis
Part of the core of statistics, linear models are used to make predictions and explain the relationship between the response and the predictors. Understanding linear models is crucial to a broader competence in the practice of statistics. Linear Models with R, Second Edition explains how to use linear models in physical science, engineering, social science, and business applications. The book incorporates several improvements that reflect how the world of R has greatly expanded since the publication of the first edition.
New to the Second Edition
- Reorganized material on...
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...
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