1. Section 4.2

2. Correlation and Regression Describe only linear relationship. Strongly influenced by extremes in data. Always plot data first. Extrapolation – Use of regression line or curve outside the values of the domain of explanatory variable.

3. Averaged Data Correlations based on averages, not actual data, are usually too high. Smooths out data. Does not allow for scatter among individuals.

4. Lurking Variables Variables that influence the two studied variables but are not in the study. Can falsely suggest a strong relationship. Can hide a relationship. Ex: Herbal tea in nursing homes/Ice cream drowning.

5. Association does not imply Causation Strong associations cause/effect relation. Causation – Change in x causes change in y. Common Response – Both x and y respond to changes in some unobserved variable. Confounding – The effect on y of x is mixed up with effects on y of other variables.

6. Lurking Variables

7. Experiments Best way to get good evidence that x causes y. Only x is changed, while lurking variables are controlled.