2Determining if a correlation exists is only the first step in a statistical analysis More important than if a relationship exists is why it exists
3Cause and Effect Relationship A change in one variable (independent) produces a change in another variable (dependent)Examples:Lowering interest rates causes people to invest more moneyCarbon dioxide in the atmosphere causes an increase in the global temperature
4Cause and effect relationships are nice because if we want to change the dependent variable we know we can produce this by changing the independent variableSometimes there is a correlation between two variables but this is not a result of a cause and effect relationship.
5Common Cause FactorAn external variable is causing the two variables to change in the same wayExamples:The number of cases of frostbite increases as the sales of winter tires increases
6Reverse Cause and Effect Relationship The independent and dependent variables are reversedExamplesCrime rates rise as the number of people in prison rise so someone argues that releasing all the criminals will decrease the crime rateThe mayor who orders his citizens to celebrate before the World Series so their team will win
7Accidental Relationship There is a correlation between two variables but it is just a coincidenceExampleThe unemployment rate is increasing at the same time that the Blue Jays go on a winning streak
8Presumed relationship The relationship does not seem to be accidental but it is difficult to show a cause and effect or common cause relationshipExampleHeart attack rates drop as fitness clubs bring in more revenue
9Extraneous VariablesExternal variables that that affect either the independent or dependent variable (or both)These may make it difficult to determine if a causal relationship exists