---
title: "Regression"
author: "Prof. Eric A. Suess"
date: "January 27, 2021"
output:
  beamer_presentation: default
  ioslides_presentation: default
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE)
```

## Introduction

Today we will breifly discuss Regression methods and the use of Regression for Classification.

- Linear Regression/Multiple Linear Regression
- Logistic Regression
- Regression Trees
- CART

## You know about Regression

Having taken a Regression class you know about 

- **Linear Regression**
- **Multiple Regression**

What about???

- **Logistic Regression**
- **Poisson Regression**
- **Generalized Linear Models (GLMs)**

## You know about Regression


The main idea with **Regression** is to model the relationship between a dependent variable and an independent variable(s).  

To make **numeric predictions**.

The main idea with **Logisitic Regression** is to model the relationship between a 0-1 dependent variable and an independent variable(s).  

To make **classifications**.

## Lantz Chapter 6

Read over the first half of Chapter 6, this is review.

We will try the predicting *medical expenses* example.

## Dummy Variables

In R the lm() function is used to fit linear regression models 
it knows about dummy variables.  There is no extra work that is need 
to include categorical variables into a regression model.  This is
because when a categorical variable is a **factor** in R, the lm() 
function knows the dummy variables to use.

Lantz: See pages 180, 181 / 194, 195

## Understanding Regression Trees and Model Trees

The preceding Chapter, **Trees** were used for **Classification**.

Later in this Chapter, **Trees** are used for **Numeric Prediction**.

## CART

One type of tree for prediction is **CART**, Classification and Regression Trees.

This is a bit of a misnomer, Linear Regresion methods are not used.  Predictions are made based on the average value of examples that reach a leaf.

## Model Trees

A second type of tree for prediction is known as **Model Trees**.

These were developed later, are less widely used but may be more powerful.

A **multiple linear regression model** is built from the exmples reaching that node.

## Trees are an alternative to Regression Modeling

Trees can make predictions and can be considered as an **alternative** to regression modeling.

## How are Trees built

The data are partitioned using a **divide-and-conquer** strategy according to the feature that will result in the greatest increase in **homogeneity** in the outcome after a split is performed.

For Classification Trees **entropy** is used.

For Numeric Decision Trees statistics such as **standard deviation** are used.

## Example

Today we will fit a **multiple linear regression model** for the
insurance data.

## Example

We will look at the application of **Regression Trees** to the wine rating data.

The **rpart** package will be used.