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R for Multivariate Analysis
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• Title: R for Multivariate Analysis
• Author(s) Avril Coghlan
• Publisher: Self Publishing; eBook (Creative Commons Licensed)
• Paperback: N/A
• eBook: PDF
• Language: English
• ISBN-10: N/A
• ISBN-13: N/A

Book Description

This book explores the correct application of these methods so as to extract as much information as possible from the data at hand, particularly as some type of graphical representation, via the R software.

• N/A

• Using R With Multivariate Statistics (Randall E. Schumacker)

This book is a quick guide to using R, free-access software available for Windows and Mac operating systems that allows users to customize statistical analysis, provides data analysis examples, R code, computer output, and explanation of results.

• R for Time Series (Avril Coghlan)

Build efficient forecasting models using traditional time series models and machine learning algorithms. This book explores the basics of time series analysis with R and lays the foundations you need to build forecasting models.

• Linear Regression Using R: An Introduction to Data Modeling

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.

• Regression Models for Data Science in R (Brian Caffo)

The book gives a rigorous treatment of the elementary concepts of regression models from a practical perspective. The ideal reader for this book will be quantitatively literate and has a basic understanding of statistical concepts and R programming.

• Practical Regression and Anova Using R (Julian J. Faraway)

The emphasis of this book is on the practice of regression and analysis of variance. The objective is to learn what methods are available and more importantly, when they should be applied.

• Beyond Multiple Linear Regression: Linear & Multilevel Models in R

This book is designed for undergraduate students who have successfully completed a multiple linear regression course, helping them develop an expanded modeling toolkit that includes non-normal responses and correlated structure.

• Forecasting, Principles and Practice, Using R (R. J. Hyndman)

This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly. Examples use R with many data sets taken from the authors' own consulting experience.

• A First Course on Time Series Analysis with SAS (Michael Falk, et al)

A unique feature of this book is its integration with the statistical software package SAS® (Statistical Analysis System) computing environment. Basic applied statistics is assumed through multiple regression.

• Using R for Data Analysis and Graphics (J H Maindonald)

This book guides users through the practical, powerful tools that the R system provides. The emphasis is on hands-on analysis, graphical display, and interpretation of data. There are many worked examples from real-world.

• R Graphics Cookbook: Practical Recipes for Visualizing Data

This cookbook provides more than 150 recipes to help scientists, engineers, programmers, and data analysts generate high-quality graphs quickly - without having to comb through all the details of R's graphing systems.

• R Programming for Data Science (Roger D. Peng)

This book is about the fundamentals of R programming. Get started with the basics of the language, learn how to manipulate datasets, how to write functions, and how to debug and optimize code. You will have a solid foundation on data science toolbox.

• R for Data Science: Visualize, Model, Transform, Tidy, Import

This book teaches you how to do data science with R: You'll learn how to get your data into R, get it into the most useful structure, transform it, visualize it and model it, how data science can help you work with the uncertainty and capture the opportunities.

• An Introduction to R (Alex Douglas, et al.)

The main aim of this book is to help you climb the initial learning curve and provide you with the basic skills and experience (and confidence!) to enable you to further your experience in using R.

This book helps you understand how R works at a fundamental level. Designed for R programmers who want to deepen their understanding of the language, and programmers experienced in other languages to understand what makes R different and special.

• Advanced R Solutions (Malte Grosser, et al)

This book offers solutions to the exercises from Advanced R, 2nd Edition by Hadley Wickham. It is work in progress and under active development. The 2nd edition of Advanced R is in print now and we hope to provide most of the answers.

• Hands-On Programming with R: Functions and Simulations

This book not only teaches you how to program, but also shows you how to get more from R than just visualizing and modeling data. Youâ€™ll gain valuable programming skills and support your work as a data scientist at the same time.

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