# Predictor variables

Variables believed to predict or correlate with the outcome variable.

## Independent variables in traditional regression models

It can be useful to distinguish between the following different types of predictor variables:

• Exposure of interest variables that are of direct interest when trying to understand the market. For example:
• Price if predicting sales.
• Educational level if predicting success in life.
• Confounding variables which, if ignored, cause incorrect conclusions to be reached about the relationship between the exposure of interest variables and the dependent variable. For example:
• If trying to understand the relationship between price and sales then 'out of stocks' (i.e., when the product is not available due to being sold out) is a confounding variable.
• If trying to understand the relationship between educational level on success in life then parent's success in life is a confounding variable.
• Precision variables' are unrelated to the exposure of interest but explain some of the variability in the dependent variable and thus their inclusion in a model can allow more precise estimates of the relationship between the other predictor variables and the outcome variable (i.e., reduce standard errors and increase the power of any statistical tests). Precision variables are only worthwhile where they have a very strong relationship with the dependent variable.

## Predictor variables in experiments

In experiments, there can be a number of further types of predictor variables:

• Alternatives (where the resulting parameter is referred to as the alternative specific constant).
• Attributes.
• Variables created by multiplying attributes with demographics (i.e., interactions).

Variables used to model differences in the parameters of mixing distributions (these are sometimes referred to as concomitant variables and covariates where the mixing distribution is discrete, such as with latent class models).

## Also known as

• Independent variables
• Regressors
• Explanatory variables
• Explanators
• Covariates
• Factors
• Regression weights (although this name should be avoided, as it can too easily be confused with the Category:''weight'' variable).
• Concomitant variables (in some contexts concomitant variables are a synonym for Supplementary Variables)