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30 March Wednesday- Lecturer: Dr Usha Sridhar
On this page, a collection of key learning is as follows:
Scheduling of content [from the Subject Outline]: Correlation, Causation, Descriptive statistics
Sources & Learning resources and activities: Paper handouts - Group Task
-Min, Median, Max
-Symmetry in data
the distance from Min to Median equals the distance from the Median to Max
left & right skewed distributions QUIZLET
-"Investigation: Does Eating Chocolate Make You Smarter?"
In fancy statistical language, a link between two variables is called a correlation. Causation, or causality, is the capacity of one variable to influence another.
However, the link between variables could happen in different directions, therefore, the correlation does not imply causation.
The link between chocolate (A) and Nobel Prizes (B) and Other factors (Cn) could happen in a number of different ways:
1. A causes B: Chocolate consumption causes Nobel Prizes. We might suggest that substances found in chocolate improve brain activity, thus leading to more research breakthroughs by scientists. In this way, we get the relationship depicted in the graph: countries like Switzerland eat a lot of chocolate and win a lot of Nobel Prizes.
2. B causes A: Nobel Prizes cause chocolate consumption. We might suggest that when people from a particular country win a Nobel Prize, the people in that country celebrate by eating lots of chocolate. We would still see the same relationship depicted in the graph: countries like Switzerland win a lot of Nobel Prizes and eat lots of chocolate.
3. C causes A and B: Some other factor causes both Nobel Prizes and Chocolate consumption. For example, we might notice that Nobel Prizes are more frequent in countries which are quite cold (like Norway and Sweden). It is easier to store and eat chocolate when it is cold than when it is hot, so people living in cold countries might eat more chocolate. It is also easier to concentrate in cold weather than in hot weather, so people living in cold countries might be able to do research better and win more Nobel Prizes. In this way, we still get the same graph: Switzerland is a cold country, and so people eat lots of chocolate and win lots of Nobel Prizes.
The format could be used to explain correlation; it means the link between variables A, B or other factors Cn (n>=1)
1. A causes B
2. B causes A
3. C causes A and B
4. Which of the above three explanations do you think is the most likely explanation for the correlation?
As mentioned, causation, also called causality, is the capacity of one variable to influence another.
Correlation does not imply causation
As you can see, just because two things are linked, this does not mean that one definitely causes the other. Just because two variables are correlated, this does not mean that one causes the other.
As discussed [the information in the article], the link between chocolate and Nobel Prizes is most likely a case where C [another influence causes A and B]. The variable C in this case is wealth, as people in richer countries have more money to spend on luxury foods like chocolate, and also have more money to spend on research. To show this, the authors of this article also did a similar study on sales of luxury cars, and found that they too correlated well with Nobel Prizes.
Activity: Video watching - TEDx talk and analysis 5'57 [Published on Nov 5, 2012 - 87,670 views on 6 April 2015]
YouTube link: https://www.youtube.com/watch?v=8B271L3NtAw
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