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Basic Concepts – Simple Linear Regression

In Chapter 1, Getting Started with Regression, we understood the concept of regression through the basic principles that govern its algorithms. Moreover, we were been able to discover the different types of regression that make it a real family of algorithms that can solve the most varied types of problems. In this book, we will learn more about all of them, but for now, let us begin with the basic concepts from the simpler algorithm, as indicated by its name: simple linear regression.

As we will see, simple linear regression is easy to understand but represents the basis of regression techniques; once these concepts are understood, it will be easier for us to address the other types of regression. To begin with, let's take an example of applying linear regression taken from the real world.

Consider some data...

Key benefits

Implement different regression analysis techniques to solve common problems in data science - from data exploration to dealing with missing values

From Simple Linear Regression to Logistic Regression - this book covers all regression techniques and their implementation in R

A complete guide to building effective regression models in R and interpreting results from them to make valuable predictions

Description

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.

Who is this book for?

This book is intended for budding data scientists and data analysts who want to implement regression analysis techniques using R. If you are interested in statistics, data science, machine learning and wants to get an easy introduction to the topic, then this book is what you need! Basic understanding of statistics and math will help you to get the most out of the book. Some programming experience with R will also be helpful

What you will learn

Get started with the journey of data science using Simple linear regression

Deal with interaction, collinearity and other problems using multiple linear regression

Understand diagnostics and what to do if the assumptions fail with proper analysis

Load your dataset, treat missing values, and plot relationships with exploratory data analysis

Develop a perfect model keeping overfitting, under-fitting, and cross-validation into consideration

Deal with classification problems by applying Logistic regression

Explore other regression techniques – Decision trees, Bagging, and Boosting techniques

Learn by getting it all in action with the help of a real world case study.

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

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Total R$ 764.97

Hitomi Cranford Oct 27, 2018

I was hoping for more in terms of multicollinearity. Discussion of the tidyverse library to build amazing multiple linear regressions would have been awesome but that doesn't take away from the fact that this book is a really good start!

Amazon Verified review

Vincenzo Ferraro Mar 13, 2018

This book has a lot to offer both in terms of theory and code. It is a simple book that does not require a great deal of knowledge of R. The most widespread statistical techniques are treated in a simple way. There are also many examples available to immediately apply the concepts learned.

TR Mar 13, 2018

I ordered this book to prepare for a university exams and I found it really interesting and very clear. For those who want to have a base I highly recommend it. In addition to explaining the concepts well is full of examples that seriously help to understand the operation of various algorithms.

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Regression Analysis with R: Design and develop statistical nodes to identify unique relationships within data at scale

What do you get with Print?

Regression Analysis with R

Basic Concepts – Simple Linear Regression

Association between variables – covariance and correlation

Searching linear relationships

Least squares regression

Creating a linear regression model

Modeling a perfect linear association

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Regression Analysis with R: Design and develop statistical nodes to identify unique relationships within data at scale

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