1. Cause and Effect

2. Determining if a correlation exists is only the first step in a statistical analysis
More important than if a relationship exists is why it exists

3. Cause 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 money
Carbon dioxide in the atmosphere causes an increase in the global temperature

4. Cause and effect relationships are nice because if we want to change the dependent variable we know we can produce this by changing the independent variable
Sometimes there is a correlation between two variables but this is not a result of a cause and effect relationship.

5. Common Cause Factor
An external variable is causing the two variables to change in the same way
Examples:
The number of cases of frostbite increases as the sales of winter tires increases

6. Reverse Cause and Effect Relationship
The independent and dependent variables are reversed
Examples
Crime rates rise as the number of people in prison rise so someone argues that releasing all the criminals will decrease the crime rate
The mayor who orders his citizens to celebrate before the World Series so their team will win

7. Accidental Relationship
There is a correlation between two variables but it is just a coincidence
Example
The unemployment rate is increasing at the same time that the Blue Jays go on a winning streak

8. Presumed relationship
The relationship does not seem to be accidental but it is difficult to show a cause and effect or common cause relationship
Example
Heart attack rates drop as fitness clubs bring in more revenue

9. Extraneous Variables
External variables that that affect either the independent or dependent variable (or both)
These may make it difficult to determine if a causal relationship exists

10. Practice/Homework
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