# 5 Differences between Independent and Dependent variables in Statistics Plus Examples

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The dependent variable, also referred to as the explained variable is the variable who’s outcome the researcher wants. The independent variable, also known as the explanatory variable is used to predict the outcomes of the dependent variable. In other words, the independent variables are used to explain the outcomes of the dependent variable in the case of deterministic model and used to predict the outcome of a dependent variable in the case of a predictive model.

Example of Dependent  and Independent Variable:

A study finds that reading levels are affected by whether a person is born in the U.S or in a foreign country, the independent variable is where the person was born and the dependent variable is their reading level. The reading level depends on where the person was born.

In this article, get to understand more details about the Independent and dependent variables, their representation similarities, examples and other related details.

## The Differences

### Definition

An independent variable is the variable that is changed or controlled in a scientific experiment to test the effects on the dependent variable. On the other hand, a dependent variable is the variable being tested and measured in a scientific experiment.

### Ability to be changed

The values of the independent variable can be changed or manipulated as per the requirements by the experimenter whereas; the values of independent variables cannot be manipulated.

### Key difference

An independent variable is a presumed cause whereas dependent variable is a measured effect.

### Representation

In a simple linear regression, ‘y’ denotes dependent variable while ‘x’ denotes independent variable which means y depends on x.

### Example

Example 1

An example of a dependent variable is how tall you are at different ages. The dependent variable (height) depends on the independent variable (age).

Example 2

You are interested in how stress affects heart rate in humans. Your independent variable would be stress while the dependent variable would be the heart rate. You can directly manipulate the stress levels in your human subject and measure how those levels change heart rate.

## Similarities between independent and dependent variables

• Both dependent and independent variables could have same outcome, such as binary.
• In many cases such in a simple regression, the dependent and independent variable are single. However, they can be many especially where the researcher is doing a multivariate or binary analytics.

## Understanding the differences between Independent and dependent Variables in research (Comparison Table)

 Points of Difference Dependent Variable Independent Variable Definition A dependent variable is the variable being tested and measured in a scientific experiment. An independent variable is the variable that is changed or controlled in a scientific experiment to test the effects on the dependent variable. Ability to be changed The values of independent variables cannot be manipulated. The values of the independent variable can be changed or manipulated as per the requirements by the experimenter. Main Difference Dependent variable is a measured effect. An independent variable is a presumed cause. Representation In a simple linear regression, ‘y’ denotes dependent variable. ‘x’ denotes independent variable which means y depends on x. Example An example of a dependent variable is how tall you are at different ages. The dependent variable (height) depends on the independent variable (age). The dependent variable (height) depends on the independent variable (age).

## Summary

### What is the main difference between dependable and Independent Variable in research?

An independent variable is the variable that is changed or controlled in a scientific experiment to test the effects on the dependent variable. On the other hand, a dependent variable is the variable being tested and measured in a scientific experiment.

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