# Correlation and Linear Regression

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?  Please describe.

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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.

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