MDM4U
Ms. Kueh
Cause and Effect Relationships
Read pg. 195-197 (up to the end of example 1) and fill in the following definitions:
Cause – and – Effect Relationship: A change in 𝑥 produces a change in 𝑦.
Example:
Common Cause Factor: An external variable causes two variables to change in the same way.
Example:
Reverse Cause – and – Effect Relationship: The dependent and independent variables are
reversed in the process of establishing causality.
Example:
Accidental Relationship: A correlation exists without any causal relationship between variables.
Example:
Presumed Relationship: A correlation does not seem to be accidental even through no cause –
and – effect relationship or common-cause factor is apparent.
Example:
Example 1 Identify the most likely type of causal relationship between each of the following
pairs of variables. Assume that a strong positive correlation has been observed with the first
variable as the independent variable.
a) Time spent practicing free-throws, free-throw scoring percent.
b) Aptitude for physics, aptitude for chemistry
c) Amount of traffic congestion, number of accidents
d) Price of milk, crime rate
Example 2 For each pair of variables, identify a possible common- cause factor that could
account for a positive linear correlation.
a) Ice cream sales, corn harvest
b) Number of traffic accidents, number of traffic violations
c) Confidence level facing a final exam, final exam score
d) Endurance on the tennis court, tennis skill
2. Answers may vary. Typical answers are
b) driving habits or traffic volume
d) time spent practicing
a) temperature
c) amount of studying
Answers: 1. a) cause and effect
b) common-cause factor
c) cause and effect, common-cause factor, or reverse cause and effect are all possible.
d) accidental
Homework: pg. 199 #1-7, 14