4.2 Cautions about Correlation and Regression Correlation and regression are powerful tools for

1. 4.2 Cautions about Correlation and Regression Correlation and regression are powerful tools for describing the relationship between two variables. When you use these tools, you must be aware of their limitations, beginning with the fact that correlation and regression describe only linear relationships. Also remember that the correlation r and the least-squares regression line are not resistant.

2. Extrapolation Extrapolation is the use of a regression line prediction far outside the domain of values of the explanatory variable x that you used to obtain the line or curve. Such predictions are often not accurate. Example: Deriving an equation for baby weight and age where the age only goes up to 12 months. Then trying to predict a baby weight at 16 months.

3. Lurking Variables A lurking variables is a variable that is not among the explanatory or response variables in a study and yet may influence the interpretation of relationship among those variables. A lurking variable can falsely suggest a strong relationship between x and y or it can hide a relationship that is really there.

4. The question of Causation In many studies of the relationship between two variables, the goal is to establish that changes in the explanatory variable cause changes in the response variable. Even when a strong association is present, the conclusion that this association is due to a causal link between the variables is often elusive.

5. Explaining Association: causation X Y Causation: changes in x cause a change in y x = y = Note: rarely will you find a direct causation relationship. Just about every relationship has more than one variable causing the change.

6. Common Response Common Response: changes in both x and y are caused by changes in a lurking variable z. X Y Z

7. Confounding Confounding: The effects (if any) of x on y is confounded with the effect of a lurking variable z. ? X Y Z