A dependent variable, often denoted as Y, is the variable in a mathematical relationship, statistical model, or experiment that is being predicted or explained. It's considered "dependent" because its value depends on the values of one or more other variables, which are known as independent variables.
In a mathematical equation, the dependent variable is often the output or the result of the equation.
For example, in the simple linear equation:
Y = aX + b
Y is the dependent variable. It's what we are trying to predict or explain.
X is the independent variable. It's the predictor we are using to estimate Y.
a is the coefficient of X, representing how much Y changes for a one-unit change in X.
b is the constant term, sometimes called the y-intercept, representing the value of Y when X is zero.
The dependent variable is what you measure in the experiment and what changes as a result of the manipulations you make to the independent variable(s). In other words, it's the output or outcome whose variation you're interested in understanding.
In statistical modeling and machine learning, we often use the independent variables to estimate or predict the dependent variable. For example, in a regression model, we would estimate the dependent variable (Y) as a function of one or more independent variables.
In a scientific experiment, the dependent variable is the measure that you think will be affected by manipulation of the independent variable. For instance, if you are studying the effect of different amounts of light on the growth of a plant, the growth of the plant (e.g., its height) would be the dependent variable, and the amount of light would be the independent variable.
Updated 5 months ago