Regression Analysis With R by Ciaburro, Giuseppe -- Read -- Imperial Library of Trantor

Index

Title Page Copyright and Credits

Regression Analysis with R

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Contributors

About the author About the reviewer Packt is searching for authors like you

Preface

Who this book is for What this book covers To get the most out of this book

Download the example code files Download the color images Conventions used

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Reviews

Getting Started with Regression

Going back to the origin of regression Regression in the real world Understanding regression concepts Regression versus correlation Discovering different types of regression The R environment Installing R

Using precompiled binary distribution

Installing on Windows Installing on macOS Installing on Linux

Installation from source code

RStudio R packages for regression

The R stats package The car package The MASS package The caret package The glmnet package The sgd package The BLR package The Lars package

Summary

Basic Concepts – Simple Linear Regression

Association between variables – covariance and correlation Searching linear relationships Least squares regression Creating a linear regression model

Statistical significance test Exploring model results Diagnostic plots

Modeling a perfect linear association Summary

More Than Just One Predictor – MLR

Multiple linear regression concepts Building a multiple linear regression model Multiple linear regression with categorical predictor

Categorical variables Building a model

Gradient Descent and linear regression

Gradient Descent Stochastic Gradient Descent The sgd package Linear regression with SGD

Polynomial regression Summary

When the Response Falls into Two Categories – Logistic Regression

Understanding logistic regression

The logit model

Generalized Linear Model

Simple logistic regression

Multiple logistic regression

Customer satisfaction analysis with the multiple logistic regression Multiple logistic regression with categorical data

Multinomial logistic regression Summary

Data Preparation Using R Tools

Data wrangling

A first look at data Change datatype Removing empty cells Replace incorrect value Missing values Treatment of NaN values

Finding outliers in data Scale of features

Min–max normalization z score standardization

Discretization in R

Data discretization by binning Data discretization by histogram analysis

Dimensionality reduction

Principal Component Analysis

Summary

Avoiding Overfitting Problems - Achieving Generalization

Understanding overfitting

Overfitting detection – cross-validation

Feature selection

Stepwise regression Regression subset selection

Regularization

Ridge regression Lasso regression ElasticNet regression

Summary

Going Further with Regression Models

Robust linear regression Bayesian linear regression

Basic concepts of probability Bayes' theorem Bayesian model using BAS package

Count data model

Poisson distributions Poisson regression model Modeling the number of warp breaks per loom

Summary

Beyond Linearity – When Curving Is Much Better

Nonlinear least squares Multivariate Adaptive Regression Splines Generalized Additive Model Regression trees Support Vector Regression Summary

Regression Analysis in Practice

Random forest regression with the Boston dataset

Exploratory analysis Multiple linear model fitting Random forest regression model

Classifying breast cancer using logistic regression

Exploratory analysis Model fitting

Regression with neural networks

Exploratory analysis Neural network model

Summary

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