## Introduction

The *Poisson Regression* is used to model count data with the assumption that the dependent variable has a Poisson distribution. It is also known as the log-linear model.

This article describes how to create a *Poisson *Regression output as shown below. The example below is a Poisson regression that models a survey respondent’s number of fast-food occasions based on characteristics like age, gender, and work status.

## Requirements

- A count
**Outcome**variable with at least three outcomes to be predicted. Ideally, a Numeric variable. When using stacked data the**Outcome**variable should be a single question in a Multi type structure (eg. Numeric-Multi). - Continuous, categorical, or binary
**Predictors**variables will be considered as predictors of the outcome variable. When using stacked data the*Predictor(s)*need to be a single question in a Grid type structure (Binary - Grid).

## Method

- Go to
**Anything > Advanced Analysis >****Regression > Poisson Regression.** - In the
**object inspector**go to the**Inputs**tab. - In the
**Output**menu select the binary variable to be predicted by the*predictor variables.* - Select the predictor variable(s) from the
**Predictor(s)**list. - OPTIONAL: Select the desired
**Output**type:**Summary**: The default; as shown in the example above.**Detail**: Typical R output, some additional information compared to**Summary**, but without the pretty formatting.**ANOVA**: Analysis of variance table containing the results of Chi-squared likelihood ratio tests for each predictor.**Relative Importance Analysis**: The results of a relative importance analysis.**Effects Plot**Plots the relationship between each of the*Predictors*and the*Outcome*.

- OPTIONAL: Select the desired
**Missing Data**treatment. (See Missing Data Options). - OPTIONAL: Select
**Variable names**to display variable names in the output instead of labels. - OPTIONAL: Select
**Correction**. Choose between**None**(the default),**False Discovery Rate**,**Bonferroni**. - OPTIONAL: Specify the
**Automated outlier removal**percentage to remove possible outliers. - OPTIONAL: Select
**Stack data**to stack the input data prior to analysis. Stacking can be desirable when each individual in the data set has multiple cases and an aggregate model is desired.

## See Also

How to Run Binary Logit Regression

How to Run a Generalized Linear Model

How to Run a Multinomial Logit Regression

How to Run NBD Regression in Displayr

How to Run Ordered Logit Regression

How to Run Quasi-Poisson Regression

How to Run a Stepwise Regression

How to Create Regression Multicollinearity Table (VIF)

How to Create a Prediction-Accuracy Table

How to Create a Goodness-of-Fit Plot

How to Test Residual Heteroscedasticity of Regression Models

How to Save Predicted Values of Regression Models

How to Save Fitted Values of Regression Models

How to Save Probabilities of Each Response of Regression Models

How to Test Residual Normality (Shapiro-Wilk) of Regression Models

How to Test Residual Serial Correlation (Durbin-Watson) of Regression Models

How to Save Residuals of Regression Models

## Comments

0 comments

Article is closed for comments.