Section 4.2

Correlation and RegressionDescribe 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.

Averaged DataCorrelations based on averages, not actual

data, are usually too high.Smooths out data.Does not allow for scatter among individuals.

Lurking VariablesVariables 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.

Association does not imply CausationStrong 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.

ExperimentsBest way to get good evidence that x causes

y.Only x is changed, while lurking variables are

controlled.