*Regression* models the relationship between a dependent variable and one or more independent variables. There are many different types of Regression models. Displayr offers its users to select between seven types of Regression models. Additionally, any of these models can be run in a stepwise mode.

The type of regression depends on the type and number of outcomes the model is aiming to predict.

## Requirements

- An
**Outcome**variable. When using stacked data the**Outcome**variable should be a single question in a Multi type structure. -
**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.

## Method

- The type of Regression type required will depend on the outcome variable used in your Regression model. To find the
**Outcome**variable select the output and go**Inputs > Linear Regression > Outcome**. - OPTIONAL: To view all potential outcomes of the dependent variable go to the
**Data Sets**tree, select the**Outcome**variable and go to the**object inspector > Properties > DATA VALUES > Values**. Alternatively, you can also drag and drop the variable onto the**Page**to create a table containing all outcomes. - Go to
**Inputs > Regression Type**and select the appropriate model depending on the number of categories of the**Outcome variable**.

**Outcome variable****Example****Regression Type**Two categories 1. Yes / Selected

2. No / Not selected

Three to 11 ordered

categories1. Hate

2. Dislike

3. Acceptable

4. Like

5. LoveOrdered logit Three to 11 unordered

categories1. Africa

2. Australia

3. Asia

4. America

5. EuropeMultinomial Logit Regression 12 or more ordered

categoriesHow would you rate your

happiness on a scale of 0 to

100Linear regression Net Promoter Score

(NPS)-100: Detractor

0: Passive/Neutral

100: PromoterLinear regression Purchase or usage

quantitiesNumber of cans of coke

consumed per weekNBD (or, if you get a weird

message, quasi-Poisson

regression)Count data with the assumption that the dependent variable has a Poisson distribution The number of births per hour during a given day

Poisson Regression Count data with overdispersed distribution Overdispersion occurs when the observed variance is higher than the variance of a theoretical model Quasi-Poisson

regression

## See Also

How to Select the Regression Type for Driver Analysis