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In statistics, a **regressor** is the name given to any variable in a regression model that is used to predict a response variable.

A regressor is also referred to as:

- An explanatory variable
- An independent variable
- A manipulated variable
- A predictor variable
- A feature

All of these terms are used interchangeably depending on the type of field you’re working in: statistics, machine learning, econometrics, biology, etc.

**Note:** Sometimes a response variable is called a “regressand.”

**Regressors in Regression Models**

Most regression models take the following form:

**Y = β _{0} + B_{1}x_{1}+ B_{2}x_{2} + B_{3}x_{3} + ε**

where:

**Y:**The response variable**β**The coefficients for the regressors_{i}:**x**The regressors_{i}:**ε:**The error term

The whole point of building a regression model is to understand how changes in a regressor lead to changes in a response variable (or “regressand”).

Note that regression models can have one or more regressors.

When there is only one regressor, the model is referred to as a simple linear regression model and when there are multiple regressors, the model is referred to as a multiple linear regression model to indicate that there are *multiple* regressors.

The following examples illustrate how to interpret regressors in different regression models.

**Example 1: Crop Yield**

Suppose a farmer is interested in understanding the factors that affect total crop yield (in pounds). He collects data and builds the following regression model:

Crop Yield = 154.34 + 3.56*(Pounds of Fertilizer) + 1.89*(Pounds of Soil)

This model has two regressors: Fertilizer and Soil.

Here’s how to interpret these two regressors:

**Fertilizer:**For each additional pound of fertilizer used, crop yield increases by an average of 3.56 pounds, assuming the amount of soil is held constant.**Soil:**For each additional pound of soil used, crop yield increases by an average of 1.89 pounds, assuming the amount of fertilizer is held constant.

**Example 2: Exam Scores**

Suppose a professor is interested in understanding how the amount of hours studied affects exam scores. He collects data and builds the following regression model:

Exam Score = 68.34 + 3.44*(Hours Studied)

This model has one regressor: Hours studied. We interpret the coefficient for this regressor to mean that for each additional hour studied, exam score increases by an average of 3.44 points.

**Additional Resources**

How to Interpret Regression Coefficients

How to Test the Significance of a Regression Slope

How to Read and Interpret a Regression Table