Regression for a qualitative binary response variable $$(Y_i = 0$$ or $$1)$$. The explanatory variables can be either quantitative or qualitative.

### Simple Logistic Regression Model Regression for a qualitative binary response variable $$(Y_i = 0$$ or $$1)$$ using a single (typically quantitative) explanatory variable.

#### Overview

The probability that $$Y_i = 1$$ given the observed value of $$x_i$$ is called $$\pi_i$$ and is modeled by the equation

$P(Y_i = 1|\, x_i) = \frac{e^{\beta_0 + \beta_1 x_i}}{1+e^{\beta_0 + \beta_1 x_i}} = \pi_i$

The coefficents $$\beta_0$$ and $$\beta_1$$ are difficult to interpret directly. Typicall $$e^{\beta_0}$$ and $$e^{\beta_1}$$ are interpreted instead. The value of $$e^{\beta_0}$$ or $$e^{\beta_1}$$ denotes the relative change in the odds that $$Y_i=1$$. The odds that $$Y_i=1$$ are $$\frac{\pi_i}{1-\pi_i}$$.

#### Explanation

Simple Logistic Regression is used when

• the response variable is binary $$(Y_i=0$$ or $$1)$$, and
• there is a single explanatory variable $$X$$ that is typically quantitative but could be qualitative (if $$X$$ is binary or ordinal).

#### The Model

Since $$Y_i$$ is binary (can only be 0 or 1) the model focuses on describing the probability that $$Y_i=1$$ for a given scenario. The probability that $$Y_i = 1$$ given the observed value of $$x_i$$ is called $$\pi_i$$ and is modeled by the equation

$P(Y_i = 1|\, x_i) = \frac{e^{\beta_0 + \beta_1 x_i}}{1+e^{\beta_0 + \beta_1 x_i}} = \pi_i$

The assumption is that for certain values of $$X$$ the probability that $$Y_i=1$$ is higher than for other values of $$X$$.

#### Interpretation

This model for $$\pi_i$$ comes from modeling the log of the odds that $$Y_i=1$$ using a linear regression, i.e., $\log\underbrace{\left(\frac{\pi_i}{1-\pi_i}\right)}_{\text{Odds for}\ Y_i=1} = \underbrace{\beta_0 + \beta_1 x_i}_{\text{linear regression}}$ Beginning to solve this equation for $$\pi_i$$ leads to the intermediate, but important result that $\underbrace{\frac{\pi_i}{1-\pi_i}}_{\text{Odds for}\ Y_i=1} = e^{\overbrace{\beta_0 + \beta_1 x_i}^{\text{linear regression}}} = e^{\beta_0}e^{\beta_1 x_i}$ Thus, while the coefficients $$\beta_0$$ and $$\beta_1$$ are difficult to interpret directly, $$e^{\beta_0}$$ and $$e^{\beta_1}$$ have a valuable interpretation. The value of $$e^{\beta_0}$$ is interpreted as the odds for $$Y_i=1$$ when $$x_i = 0$$. It may not be possible for a given model to have $$x_i=0$$, in which case $$e^{\beta_0}$$ has no interpretation. The value of $$e^{\beta_1}$$ denotes the proportional change in the odds that $$Y_i=1$$ for every one unit increase in $$x_i$$.

Notice that solving the last equation for $$\pi_i$$ results in the logistic regression model presented at the beginning of this page.

#### Hypothesis Testing

Similar to linear regression, the hypothesis that $H_0: \beta_1 = 0 \\ H_a: \beta_1 \neq 0$ can be tested with a logistic regression. If $$\beta_1 = 0$$, then there is no relationship between $$x_i$$ and the log of the odds that $$Y_i = 1$$. In other words, $$x_i$$ is not useful in predicting the probability that $$Y_i = 1$$. If $$\beta_1 \neq 0$$, then there is information in $$x_i$$ that can be utilized to predict the probability that $$Y_i = 1$$, i.e., the logistic regression is meaningful.

#### Checking Model Assumptions

The model assumptions are not as clear in logistic regression as they are in linear regression. For our purposes we will focus only on considering the goodness of fit of the logistic regression model. If the model appears to fit the data well, then it will be assumed to be appropriate.

##### Deviance Goodness of Fit Test

If there are replicated values of each $$x_i$$, then the deviance goodness of fit test tests the hypotheses $H_0: \pi_i = \frac{e^{\beta_0 + \beta_1 x_i}}{1+e^{\beta_0 + \beta_1 x_i}}$ $H_a: \pi_i \neq \frac{e^{\beta_0 + \beta_1 x_i}}{1+e^{\beta_0 + \beta_1 x_i}}$

##### Hosmer-Lemeshow Goodness of Fit Test

If there are very few or no replicated values of each $$x_i$$, then the Hosmer-Lemeshow goodness of fit test can be used to test these same hypotheses. In each case, the null assumes that logistic regression is a good fit for the data while the alternative is that logistic regression is not a good fit.

#### Prediction

One of the great uses of Logistic Regression is that it provides an estimate of the probability that $$Y_i=1$$ for a given value of $$x_i$$. This probability is often referred to as the risk that $$Y_i=1$$ for a certain individual. For example, if $$Y_i=1$$ implies a person has a disease, then $$\pi_i=P(Y_i=1)$$ represents the risk of individual $$i$$ having the disease based on their value of $$x_i$$, perhaps a measure of their cholesterol or some other predictor of the disease.

Examples: challenger

### Multiple Logistic Regression Model Logistic regression for multiple explanatory variables that can either be quantitative or qualitative or a mixture of the two.

#### Overview

The probability that $$Y_i = 1$$ given the observed data $$(x_{i1},\ldots,x_{ip})$$ is called $$\pi_i$$ and is modeled by the equation

$P(Y_i = 1|\, x_{i1},\ldots,x_{ip}) = \frac{e^{\beta_0 + \beta_1 x_{i1} + \ldots + \beta_p x_{ip}}}{1+e^{\beta_0 + \beta_1 x_{i1} + \ldots + \beta_p x_{ip} }} = \pi_i$

The coefficents $$\beta_0,\beta_1,\ldots,\beta_p$$ are difficult to interpret directly. Typically $$e^{\beta_k}$$ for $$k=0,1,\ldots,p$$ is interpreted instead. The value of $$e^{\beta_k}$$ denotes the relative change in the odds that $$Y_i=1$$. The odds that $$Y_i=1$$ are $$\frac{\pi_i}{1-\pi_i}$$.

#### Explanation

Multiple Logistic Regression is used when

• the response variable is binary $$(Y_i=0$$ or $$1)$$, and
• there are multiple explanatory variables $$X_1,\ldots,X_p$$ that can be either quantitative or qualitative.

#### The Model

Very little changes in multiple logistic regression from Simple Logistic Regression. The probability that $$Y_i = 1$$ given the observed data $$(x_{i1},\ldots,x_{ip})$$ is called $$\pi_i$$ and is modeled by the expanded equation

$P(Y_i = 1|\, x_{i1},\ldots,x_{ip}) = \frac{e^{\beta_0 + \beta_1 x_{i1} + \ldots + \beta_p x_{ip}}}{1+e^{\beta_0 + \beta_1 x_{i1} + \ldots + \beta_p x_{ip} }} = \pi_i$

The assumption is that for certain combinations of $$X_1,\ldots,X_p$$ the probability that $$Y_i=1$$ is higher than for other combinations.

#### Interpretation

The model for $$\pi_i$$ comes from modeling the log of the odds that $$Y_i=1$$ using a linear regression, i.e., $\log\underbrace{\left(\frac{\pi_i}{1-\pi_i}\right)}_{\text{Odds for}\ Y_i=1} = \underbrace{\beta_0 + \beta_1 x_{i1} + \ldots + \beta_p x_{ip}}_{\text{linear regression}}$ Beginning to solve this equation for $$\pi_i$$ leads to the intermediate, but important result that $\underbrace{\frac{\pi_i}{1-\pi_i}}_{\text{Odds for}\ Y_i=1} = e^{\overbrace{\beta_0 + \beta_1 x_{i1} + \ldots + \beta_p x_{ip}}^{\text{liear regression}}} = e^{\beta_0}e^{\beta_1 x_{i1}}\cdots e^{\beta_p x_{ip}}$ As in Simple Linear Regression, the values of $$e^{\beta_0}$$, $$e^{\beta_1}$$, $$\ldots$$, $$e^{\beta_p}$$ are interpreted as the proportional change in odds for $$Y_i=1$$ when a given $$x$$-variable experiences a unit change, all other variables being held constant.

#### Checking the Model Assumptions

Diagnostics are the same in multiple logistic regression as they are in simple logistic regression.

#### Prediction

The idea behind prediction in multiple logistic regression is the same as in simple logistic regression. The only difference is that more than one explanatory variable is used to make the prediction of the risk that $$Y_i=1$$.

Examples: GSS