What is a Dependent Variable?
A dependent variable is one whose value varies in response to the change in the value of an independent variable. It is the outcome of an experiment or statistical analysis; hence, also termed a left-hand-side variable usually represented as ‘Y’ on the graph.
The relationship between the independent and dependent variables signifies the cause-and-effect phenomenon, where any change in the value of the former triggers a change in the latter’s value.
Table of contents
- The dependent variable is the factor, event, or value that varies when there is a change in the other variable (independent variable). It is also called a left-hand-side outcome, or response variable.
- On a graph, the left-hand-side variable is marked on the vertical line, i.e., the y axis, and is mathematically denoted as y = f(x).
- It is one of the two crucial variables of any research, i.e., science experiments, hypothesis testing, statistical analysis, or mathematical reasoning.
- Also, the dependent variable in one case can be an independent variable in another research.
Dependent Variable Explained
A dependent variable could be anything, an event, a value, or an object whose occurrence of alteration depends on the change in the independent variable. For graphical representation, the independent variable is indicated as an x-value. This is because it appears on the horizontal line—the x-axis. The outcome variable, on the other hand, is represented as a y-value on the vertical line—the y-axis. Thus, the response variable can be mathematically written as:
y = f(x)
In scientific experiments, statistics, hypothesis testing, and mathematical reasoning, results (the value of the outcome variables) are interpreted by substituting the value of the independent variables in an equation that correlates the two variables.
Besides, a researcher can substitute different values of X to get the desired response variable, i.e., Y. The analysis also determines the extent of outcome variable deviation—when the independent variable is changed. Dependent and independent variables are like the two sides of a coin—indispensable in research, analysis, and science. While the outcome variable is the effect, the independent variable is the cause behind it.
It is important to note that the outcome variable of one research can act as an
independent variable for another. For instance, high production cost results in a lower profit marginProfit MarginProfit Margin is a metric that the management, financial analysts, & investors use to measure the profitability of a business relative to its sales. It is determined as the ratio of Generated Profit Amount to the Generated Revenue Amount. for a company. The reduced profit margin causes loss to the company. Here, the lower profit margin is a response variable in the first scenario but an independent variable in the second.
The outcome variable is the one whose variation is being investigated by changing inputs; in a statistical setting, they are referred to as regressors. Stability distinguishes higher-quality variables. The effects on the variable should not change when repeated—with the same participants, conditions, and experimental manipulations.
In real-world applications, the operationalization of variables is very important. Simply put, it refers to the measurement methods—a useful variable is one that can be measured.
A research analysis can be plotted on a graph when we know the outcome variables and independent variables. The independent variables are put into the given equation to acquire the output or the outcome variable:
Y = a + bX
‘Y’ is the dependent variable;
‘a’ is the Y-intercept;
‘X’ is the independent variable; and
‘b’ is the coefficient or slope.
On a graph, these variables are represented as follows:
- Dependent variables are marked on the y-axis.
- Independent variables appear on the x-axis.
Let us assume that we have a regressionRegressionRegression Analysis is a statistical approach for evaluating the relationship between 1 dependent variable & 1 or more independent variables. It is widely used in investing & financing sectors to improve the products & services further. equation Y = 12 – 2X. The the values of outcome variables and independent variables are given below:
Y = 12 – 2X
- 12 – 2 × 1 = 10
- 12 – 2 × 2 = 8
- 12 – 2 × 3 = 6
- 12 – 2 × 4 = 4
- 12 – 2 × 5 = 2
Now, let us plot the above data on a regression graph:
Let us look at some examples to observe the correlation between independent variables and outcome variables:
A mutual fund plan provides higher returns when $10,000 is invested for a longer period. The increase in returns caused by extended investment duration is shown below:
Investment Period Estimated Return
1 Year – $1200
2 Years – $2544
3 Years – $4049
4 Years – $5735
5 Years – $7623
In the above example, the investment period is the independent variable, and the estimated return is the outcome variable. Now, let us plot it on a graph:
In the above graph, we can see that the increase in estimated returns depends upon the investment period.
There is a relationship between demand, supply, and price of a product. The price of a product is dependent on its demand and supply in the market.
Here, price is the dependent variable; it will respond to movements in independent factors i.e. demand and supply of the product.
Frequently Asked Questions (FAQs)
An outcome variable refers to an event or value whose occurrence or transition relies upon the variation in the other variable—the independent variable. On the contrary, an independent variable is the one that occurs or whose value changes on its own without the influence of other factors.
The independent variable influences the outcome variable. When the investigator alters the independent variable, its impact on the outcome variable is recorded.
For an exam, the test scores are being measured; here, test scores are the dependent variables. The outcome is dependent on tutoring—therefore acts as the independent variable. Tutoring is independent; the impact of change in tutors and tutoring style on a test score is observed.
There is a cause-and-effect relationship between independent and dependent variables; thus, if one variable triggers or affects the other, it is an independent variable. However, the other variable will be the outcome of this influence, which shows it is the dependent variable, also known as the outcome variable.
This has been a guide to what is dependent variable & its definition. We explain its applications in science and research using examples and graphs. You can learn more about it from the following articles –
- Regression LineRegression LineA regression line indicates a linear relationship between the dependent variables on the y-axis and the independent variables on the x-axis. The correlation is established by analyzing the data pattern formed by the variables.
- R-SquaredR-SquaredR-squared ( R2 or Coefficient of Determination) is a statistical measure that indicates the extent of variation in a dependent variable due to an independent variable.
- MulticollinearityMulticollinearityMulticollinearity is a statistical phenomenon in which two or more variables in a regression model are so dependent on each other that one can be linearly predicted from the other with high accuracy. It is most commonly used in observational studies and is less commonly used in experimental studies.