R: Recipes for Analysis, Visualization and Machine Learning

Chapter 3. Understanding Regression Analysis

In this chapter, we will cover the following recipes:

• Fitting a linear regression model with lm
• Summarizing linear model fits
• Using linear regression to predict unknown values
• Generating a diagnostic plot of a fitted model
• Fitting a polynomial regression model with lm
• Fitting a robust linear regression model with rlm
• Studying a case of linear regression on SLID data
• Applying the Gaussian model for generalized linear regression
• Applying the Poisson model for generalized linear regression
• Applying the Binomial model for generalized linear regression
• Fitting a generalized additive model to data
• Visualizing a generalized additive model
• Diagnosing a generalized additive model

Introduction

Regression is a supervised learning ...