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Q: What happens to r if you swap the explanatory and response variable after calculating the coefficient?

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As the explanatory variable increases, the response variable increases

True

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.after the treatment

Explanatory (or predictor) variable: A variable which is used in a relationship to explain or to predict changes in the values of another variable; the latter called the dependent variable.

The coefficient is 7 and the variable is x

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Explanatory and Response variables are just fancy words for independent and dependent variables. Explanatory is the independent variable and response is the dependent variable.

As the explanatory variable increases, the response variable increases

No. It shows changes in the response variable against changes in the explanatory (or independent) variable(s).

designed experiment

True

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.after the treatment

Answer is True... Via ApexVs

An explanatory variable is one which may be used to explain or predict changes in the values of another variable. There may be several explanatory variables.

The coefficient is in front of a variable.

Explanatory (or predictor) variable: A variable which is used in a relationship to explain or to predict changes in the values of another variable; the latter called the dependent variable.

Yes, a coefficient of a variable can be negative.

The coefficient is 7 and the variable is x