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Predictor Variable

What is a Predictor Variable?

Predictor variable, also known sometimes as the independent variable, is used to make a prediction for dependent variables. Predictor variables are extremely common in data science and the scientific method. The predictor variable is the counterpart to the dependent variable, often directly informed or affected by the predictor variable. 

How does a Predictor Variable work?

All experiments examine or deal with some form of variables. Often the variable is not only something that is measured, but it is also manipulated or transformed. The main forms of variables are predictor variables, independent variables, and dependent, or outcome variables. The predictor variable is often mistaken as the independent variable, however they vary slightly in definition. Where an independent variable may be transformed or changed throughout the experiment, the predictor variable is not. When changes do arise with predictor variables, they are often naturally occurring. 

Imagine a teacher is looking to understand the effects of missing class time on grade point average. The predictor variable is the attendance rate of the students, and the outcome variable is the grade point average. In theory, the best way to directly study the effects of attendance on grade point average is to take a select group of students and instruct them to skip class entirely, as a control. However, the likelihood of actually doing so is very slim, and borderline unethical. Alternatively, the teacher can look at the naturally occurring attendance rate, and infer the relationship to grade point average that way. By comparing students with lower attendance's GPAs with those with higher attendance rates, the teacher can come to generalized conclusions about attendance. In this situation, the teacher used a predictor variable of attendance, as the variable was not altered throughout the study.