Cause-and-Effect Relationship By: Kr, Tr, Na, Su
Definition • A change in X produces a change in Y. When it occurs: • Such relationships are sometimes clearly evident, especially in physical processes.
Example • Increasing in height from which you drop and object increases its impact velocity.
Example 2: • The rate of chemical reaction increases with temperature. Higher temperature causes faster reaction rates.
Example 3 • Increasing the speed of production line increases the number of items produced each day.
Common Cause Factor Ka, Ni, Pr, An, and Su
What is it? • The common cause factor is basically an external factor that causes changes in two variables that are unrelated in the same way.
How Does it Occur? This factor can occur by things that are beyond control such as the weather, or economy(inflation).
Example The prices of bread and bus tickets have increasedover the years, this is due to the inflation that might have occurred in both items. This way even though, these two items are completely unrelated, the inflation is the common cause of the prices increasing.
Non-example A non example would be something like for instance, the number of car accidents increasing, as well as the number of bus tickets being bought. This two variables can relate because the amount of car accidents occurring can lead people to buy bus tickets to travel, instead of having their car. This way, since they are interrelated, there is no external variable causing the change.
Definition • A correlation exists without any relationship between variables
Example #1 • The number of females enrolled in undergraduate engineering programs and the number of “reality” shows on television both increased for several years. • These two variables have a positive linear correlation, but it is likely entirely coincidental
Example #2 • Sales of cellular telephone the hole in ozone layer has been increasing over the last decade • There is no common cause between them so it is accidental that they are both going up.
Definition • A relationship in which the presumed dependent and independent variables are reversed in the process of establishing casualty • Used when the results of a relationship are opposite to the expected result
Example • A researcher observes a positive linear correlation between the amount of coffee consumed by a group of medical students and their levels of anxiety • Researcher theorizes that drinking coffee causes nervousness, but instead finds that nervous people are more likely to drink coffee
Example • People believe that an increase in pay may result in better job performance. • However, better job performance leads to increase in pay (work longer, put more effort)
Extraneous Variables • External variables that can have an effect on the data. • For example: the amount of television that is watched effect how a student does on an assessment.
Presumed Relationship • When a relationship that is not accidental and does not have evidence to conclude the type of relationship.