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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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More Than Just One Predictor – MLR

In Chapter 2, Basic Concepts – Simple Linear Regression, we understood the concept of simple linear regression that covers the relationship between only one independent variable (explanatory variable) and the dependent variable (response variable). It's not very often that we find a variable that depends solely on another. Usually, we find that the response variable depends on at least two predictors.

Let's take a look at an example. Getting to the workplace can often be a path full of variables. Scheduling your departure to arrive on time can be a difficult task. That is why you need to take different variables into account: the distance from your home, the type of route to follow (street type), traffic along the route, the number of stops (if you need to drop your children to school), weather conditions, and so on. Have...

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Regression Analysis with R
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