Please answer one of the two following questions:
1. Correlation: Correlation Does Not Mean Causation
One of the major misconceptions about correlation is that a relationship between two
variables means causation; that is, one variable causes changes in the other variable.
There is a particular tendency to make this causal error, when the two variables seem to
be related to each other.
What is one instance where you have seen correlation misinterpreted as causation?
2. Linear Regression
Linear regression is used to predict the value of one variable from another variable.
Since it is based on correlation, it cannot provide causation. In addition, the strength of
the relationship between the two variables affects the ability to predict one variable from
the other variable; that is, the stronger the relationship between the two variables, the
better the ability to do prediction.
What is one instance where you think linear regression would be useful to you in your
workplace or chosen major? Please describe including why and how it would be used.