Explanatory Variable vs Response Variable
Both terms below come up in the same part of the course, and students mix them up. Here is each one defined on its own, side by side, so you can see where they part company.
Explanatory variable
Variables and data types
An explanatory variable is the input thought to explain or predict changes in the response variable; it goes on the x-axis of a scatterplot.
The explanatory variable, also called the independent or predictor variable, is the one you treat as the cause or predictor in a relationship. It sits on the horizontal axis and supplies the values in a regression. For example, when studying how fertilizer amount affects plant height, fertilizer is the explanatory variable and height is the response. Labeling a variable explanatory reflects the question you are asking, and in an observational study it does not by itself prove that variable causes the response.
Response variable
Variables and data types
A response variable is the outcome you measure to see how it changes with the explanatory variable; it goes on the y-axis of a scatterplot.
The response variable, also called the dependent variable, is the outcome you expect to depend on the explanatory variable. It sits on the vertical axis and supplies the values in a regression, so the fitted line predicts it. For example, when studying how study time affects test scores, the test score is the response variable. A regression line estimates the mean response for each value of the explanatory variable.