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Regression Analysis with R

Regression Analysis with R

By : Giuseppe Ciaburro, Pierre Paquay, Manoj Kumar, Shaikh Salamatullah
4.7 (3)
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Regression Analysis with R

Regression Analysis with R

4.7 (3)
By: Giuseppe Ciaburro, Pierre Paquay, Manoj Kumar, Shaikh Salamatullah

Overview of this book

Regression analysis is a statistical process which enables prediction of relationships between variables. The predictions are based on the casual effect of one variable upon another. Regression techniques for modeling and analyzing are employed on large set of data in order to reveal hidden relationship among the variables. This book will give you a rundown explaining what regression analysis is, explaining you the process from scratch. The first few chapters give an understanding of what the different types of learning are – supervised and unsupervised, how these learnings differ from each other. We then move to covering the supervised learning in details covering the various aspects of regression analysis. The outline of chapters are arranged in a way that gives a feel of all the steps covered in a data science process – loading the training dataset, handling missing values, EDA on the dataset, transformations and feature engineering, model building, assessing the model fitting and performance, and finally making predictions on unseen datasets. Each chapter starts with explaining the theoretical concepts and once the reader gets comfortable with the theory, we move to the practical examples to support the understanding. The practical examples are illustrated using R code including the different packages in R such as R Stats, Caret and so on. Each chapter is a mix of theory and practical examples. By the end of this book you will know all the concepts and pain-points related to regression analysis, and you will be able to implement your learning in your projects.
Table of Contents (11 chapters)
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RStudio

To program with R, we can use any text editor and a simple command-line interface. Both of these tools are already present on any operating system, so if you don't want to install anything more, you will be able to ignore this step.

Some find it more convenient to use an Integrated Development Environment (IDE); in this case, as there are several available, both free and paid, you'll be spoilt for choice. Having to make a choice, I prefer the RStudio environment.

RStudio is a set of integrated tools designed to help you be more productive with R. It includes a console, syntax-highlighting editor that supports direct code execution, and a variety of robust tools for plotting, viewing history, debugging, and managing your workspace.

RStudio is available at the following URL: https://www.rstudio.com/.

In the following screenshot you will see the main page of the RStudio website:

This is a popular IDE available in the open source, free, and commercial versions, which works on most operating systems. RStudio is probably the only development environment developed specifically for R. It is available for all major platforms (Windows, Linux, and macOS X) and can run on a local machine such as our computer or even on the web using RStudio Server. With RStudio Server, you can provide a browser-based interface (the so-called IDE) to an R version running on a remote Linux server. It integrates several features that are really useful, especially if you use R for more complex projects or if you want to have more than one developer on a project.

The environment is made up of four different areas:

  • Scripting area: In this area we can open, create, and write our scripts
  • Console area: This zone is the actual R console where commands are executed
  • Workspace History area: In this area you can find a list of all the objects created in the workspace where we are working
  • Visualization area: In this area we can easily load packages and open R help files, but more importantly, we can view charts

The following screenshot shows the RStudio environment:

With the use of RStudio, our work will be considerably simplified, displaying all the resources needed in one window.

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Regression Analysis with R
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