1. Cautions about Correlation and Regression
Section 4.2

2. Reminders
Correlation and regression describe only linear relationships
Correlation and LSRL are not resistant

3. Extrapolation
The use of a regression line for prediction far outside the domain of values of the explanatory variable x that you used to obtain the line or curve
Predictions are often not accurate

4. Lurking Variable
A variable that is not among the explanatory or response variables in a study and yet may influence the interpretation of relationships among those variables

5. Using Averaged Data
Averaged data smooths out the variation of individual observations

6. Explaining Association
Common Response
Confounding

7. Confounding
When the variables effects on a response variable cannot be distinguished from each other
Can be explanatory or lurking variables

8. Remember:
Even when direct causation is present, it is rarely a complete explanation of an association between two variables
Very strong association between two variables is not by itself good evidence that there is a cause-and-effect link between the variables

9. Establishing Causation
The only method for determining causation is to conduct a carefully designed experiment

10. Practice Problems
pg. 238 #