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Hello, again! In this lecture we are going to talk about various types of probability distributions and what kind of events they can be used to describe. Certain distributions share features, so we group them into types. Some, like rolling a die or picking a card, have a finite number of outcomes. They follow discrete distributions. Others, like recording time and distance in track field, have infinitely many outcomes. They follow continuous distributions. We are going to examine the characteristics of some of the most common distributions. For each one we will focus on an important aspect of it or when it is used. Before we get into the specifics, you need to know the proper notation we implement when defining distributions. We start off by writing down the variable name for our set of values, followed by the tilde sign. This is superseded by a capital letter depicting the type of the distribution and some characteristics of the dataset in parenthesis. The characteristics are usu
