There are lots of potential outcome variables (the dependent variable you are predicting) that can be used when conducting a driver analysis, from five-point rating scales through to utilities from conjoint studies. To conduct a valid driver analysis we need to select an appropriate regression type or generalized linear model (GLM) which is consistent with our data.

This article describes how to select the Regression Type suitable for your Driver Analysis.

## Requirements

- A Driver Analysis output, see How to Do Driver Analysis
- Any scaled of categorical variables used should be coded in ascending order - bad to good.

## Method

- The
*Regression Type*required will depend on the outcome variable used in your Regression model. Hover over the variable in the**Inputs > Linear Regression > Outcome**field. - Click on the blue arrow that appears on hover to select the variable in the Data Sets tree to review its structure and values. In the
**object inspector**, notice the**Structure**of the variable this is a good indicator if it is categorical, binary, or numeric. - Click
**DATA VALUES > Values**button to ensure the appropriate categories and values are included in the analyses and to confirm the underlying value used for each. - Go to
**Inputs > Regression Type**and select the appropriate model based on your*Outcome*variable in the table below:

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

2. No / Not selected

Binary logit (also known as

logistic regression)Three to 11 ordered

categories1. Hate

2. Dislike

3. Acceptable

4. Like

5. LoveOrdered logit 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)Utilities from a conjoint

or MaxDiff studyThat is, a variable that

contains the estimated utilities

for each respondentLinear regression Probabilities or shares

from conjoint and

MaxDiff studiesThat is, a variable that

contains the probabilities for

each respondentUse the utilities instead of the preference shares, and then use Linear regression.

## Next

How To Automatically Remove Outliers from Regression and GLMs