Regression Analysis with R电子书

Title Page

Title Page

Copyright and Credits

Copyright and Credits

Regression Analysis with R

Regression Analysis with R

Packt Upsell

Packt Upsell

Why subscribe?

Why subscribe?

PacktPub.com

PacktPub.com

Contributors

Contributors

About the author

About the author

About the reviewer

About the reviewer

Packt is searching for authors like you

Packt is searching for authors like you

Preface

Preface

Who this book is for

Who this book is for

What this book covers

What this book covers

To get the most out of this book

To get the most out of this book

Download the example code files

Download the example code files

Download the color images

Download the color images

Conventions used

Conventions used

Get in touch

Get in touch

Reviews

Reviews

Getting Started with Regression

Getting Started with Regression

Going back to the origin of regression

Going back to the origin of regression

Regression in the real world

Regression in the real world

Understanding regression concepts

Understanding regression concepts

Regression versus correlation

Regression versus correlation

Discovering different types of regression

Discovering different types of regression

The R environment

The R environment

Installing R

Installing R

Using precompiled binary distribution

Using precompiled binary distribution

Installing on Windows

Installing on Windows

Installing on macOS

Installing on macOS

Installing on Linux

Installing on Linux

Installation from source code

Installation from source code

RStudio

RStudio

R packages for regression

R packages for regression

The R stats package

The R stats package

The car package

The car package

The MASS package

The MASS package

The caret package

The caret package

The glmnet package

The glmnet package

The sgd package

The sgd package

The BLR package

The BLR package

The Lars package

The Lars package

Summary

Summary

Basic Concepts – Simple Linear Regression

Basic Concepts – Simple Linear Regression

Association between variables – covariance and correlation

Association between variables – covariance and correlation

Searching linear relationships

Searching linear relationships

Least squares regression

Least squares regression

Creating a linear regression model

Creating a linear regression model

Statistical significance test

Statistical significance test

Exploring model results

Exploring model results

Diagnostic plots

Diagnostic plots

Modeling a perfect linear association

Modeling a perfect linear association

Summary

Summary

More Than Just One Predictor – MLR

More Than Just One Predictor – MLR

Multiple linear regression concepts

Multiple linear regression concepts

Building a multiple linear regression model

Building a multiple linear regression model

Multiple linear regression with categorical predictor

Multiple linear regression with categorical predictor

Categorical variables

Categorical variables

Building a model

Building a model

Gradient Descent and linear regression

Gradient Descent and linear regression

Gradient Descent

Gradient Descent

Stochastic Gradient Descent

Stochastic Gradient Descent

The sgd package

The sgd package

Linear regression with SGD

Linear regression with SGD

Polynomial regression

Polynomial regression

Summary

Summary

When the Response Falls into Two Categories – Logistic Regression

When the Response Falls into Two Categories – Logistic Regression

Understanding logistic regression

Understanding logistic regression

The logit model

The logit model

Generalized Linear Model

Generalized Linear Model

Simple logistic regression

Simple logistic regression

Multiple logistic regression

Multiple logistic regression

Customer satisfaction analysis with the multiple logistic regression

Customer satisfaction analysis with the multiple logistic regression

Multiple logistic regression with categorical data

Multiple logistic regression with categorical data

Multinomial logistic regression

Multinomial logistic regression

Summary

Summary

Data Preparation Using R Tools

Data Preparation Using R Tools

Data wrangling

Data wrangling

A first look at data

A first look at data

Change datatype

Change datatype

Removing empty cells

Removing empty cells

Replace incorrect value

Replace incorrect value

Missing values

Missing values

Treatment of NaN values

Treatment of NaN values

Finding outliers in data

Finding outliers in data

Scale of features

Scale of features

Min–max normalization

Min–max normalization

z score standardization

z score standardization

Discretization in R

Discretization in R

Data discretization by binning

Data discretization by binning

Data discretization by histogram analysis

Data discretization by histogram analysis

Dimensionality reduction

Dimensionality reduction

Principal Component Analysis

Principal Component Analysis

Summary

Summary

Avoiding Overfitting Problems - Achieving Generalization

Avoiding Overfitting Problems - Achieving Generalization

Understanding overfitting

Understanding overfitting

Overfitting detection – cross-validation

Overfitting detection – cross-validation

Feature selection

Feature selection

Stepwise regression

Stepwise regression

Regression subset selection

Regression subset selection

Regularization

Regularization

Ridge regression

Ridge regression

Lasso regression

Lasso regression

ElasticNet regression

ElasticNet regression

Summary

Summary

Going Further with Regression Models

Going Further with Regression Models

Robust linear regression

Robust linear regression

Bayesian linear regression

Bayesian linear regression

Basic concepts of probability

Basic concepts of probability

Bayes' theorem

Bayes' theorem

Bayesian model using BAS package

Bayesian model using BAS package

Count data model

Count data model

Poisson distributions

Poisson distributions

Poisson regression model

Poisson regression model

Modeling the number of warp breaks per loom

Modeling the number of warp breaks per loom

Summary

Summary

Beyond Linearity – When Curving Is Much Better

Beyond Linearity – When Curving Is Much Better

Nonlinear least squares

Nonlinear least squares

Multivariate Adaptive Regression Splines

Multivariate Adaptive Regression Splines

Generalized Additive Model

Generalized Additive Model

Regression trees

Regression trees

Support Vector Regression

Support Vector Regression

Summary

Summary

Regression Analysis in Practice

Regression Analysis in Practice

Random forest regression with the Boston dataset

Random forest regression with the Boston dataset

Exploratory analysis

Exploratory analysis

Multiple linear model fitting

Multiple linear model fitting

Random forest regression model

Random forest regression model

Classifying breast cancer using logistic regression

Classifying breast cancer using logistic regression

Exploratory analysis

Exploratory analysis

Model fitting

Model fitting

Regression with neural networks

Regression with neural networks

Exploratory analysis

Exploratory analysis

Neural network model

Neural network model

Summary

Summary

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