Book Image

Regression Analysis with R

By : Giuseppe Ciaburro
Book Image

Regression Analysis with R

By: Giuseppe Ciaburro

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 (15 chapters)
Title Page
Packt Upsell
Contributors
Preface
Index

Multiple linear regression with categorical predictor


After dealing with several examples of linear regression, we can certainly claim to have understood the mechanisms underlying this statistical technique. So far, we've used only continuous variables, such as predictors. What happens when the predictors are categorical variables? Don't worry, because the underlying principles of regression techniques remain the same.

Categorical variables

Categorical variables are variables that are not numerical. They do not derive from measurement operations (and do not have units of measurement), but from classification and comparison operations; for instance, they describe data that fits into specific categories. Categorical variables can be further grouped as nominal, dichotomous, or ordinal:

  • Nominal variables are variables that have two or more categories but do not have an intrinsic order. For example, the blood group variable, limited to the ABO system, can assume the values A, B, AB, and O. If we...