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In a statistical model, you have two kinds of variable. Response variables are the "outputs" of your model. Explanatory variables, on the other hand, are the "inputs" of your model. Response variables are dependent on the explanatory variables. Explanatory variable are independent of the response variables.
Imagine you were trying to formulate a statistical model of your car's fuel economy. The "output" of your model is miles per gallon (or kilometres per litre). That's your response variable. "Inputs" into your model might be (for example) engine capacity, number of cylinders, tyre pressure, etc. These are your explanatory variables. That is, fuel economy may be, or is, (to be determined by the modeling) dependent on engine capacity and/or number of cylinders and/or tyre pressure, etc.
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A The variable that changes in response to the Independent Variable?
The dependent variable.
A variable of which a researcher is not aware but which might be causing changes in the response variable is called a variable?
lurking
What are the rules for a positive correlation?
As the explanatory variable increases, the response variable increases
What axis should you put the response variable on?
y axis
In the regression model the predictor variable is useful in predicting the response variable provided that B1 equals 0?
Not useful
