**Overview**

- Covariance-a measure of how changes in one variable are associated with changes in a second variable. (measures the degree to which two variables are linearly associated)
- Positive value of covariance means two random variables tend to vary in the same direction. (positive correlation)
- Negative value of covariance means that they vary in opposite directions. (negative correlation)
- Zero value of covariance means that the don’t vary together. (uncorrelated)

- Causality- Indicates a relationship between two events
- Causation indicates that a change in the value of one variable is the cause in the change in the value of another variable (aka cause and effect)
- Correlation does not mean causation

- Causation indicates that a change in the value of one variable is the cause in the change in the value of another variable (aka cause and effect)

**Reference Links**

#### Video Transcript

Today we’re going to be talking about Covariance and Causality.

Covariance-a measure of how changes in one variable are associated with changes in a second variable. Covariance measures the degree to which two variables are linearly associated. So for example here I created a graph with Google trends showing the searches for two terms unemployment and lipstick over the past year. We could ask are these two variables related and how?

There is a such thing as a positive value of covariance. WHere two random variables vary in the same directions. So for example Ice cream consumption and shark attacks. As one increases so too do the other.

There is also a negative value of covariance. When 2 random variables vary in opposite directions. An example of this would be an increase in iron in am anemic person causes a decrease in tiredness.

And then there is a zero value of covariance. These variables do not vary together or you can say they appear uncorrelated. An example of this is Ice cream and intelligence.

Causality indicates a true relationship between two events. So there is, in essence, a cause and effect relationship established between two variables. So for example and relationship has been established between the correlation of ice cream sales and the outside temperature. However, a key thing to remember here is correlation does not necessarily mean causation.

So to review, covariance is a measure of how changes in one variable are associated with changes in a second variable. There can be a positive value of covariance whereas one increases so too does the other, a negative value of covariance, where they changes are in opposing directions and a zero value of covariance says the two values are uncorrelated.

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