Joint Distribution

A joint distribution describes how two or more random variables behave together, showing the probability of different combinations of their outcomes. For example, it could tell us the likelihood of someone being both a morning person and a coffee drinker. It provides a complete picture of their interdependence, allowing us to understand how variables relate to each other and influence joint outcomes. This concept is useful in analyzing complex data where multiple factors are involved, helping us identify patterns and dependencies between variables.