Transforming_and_combining_random_variables
Transforming and combining random variables produces predictable changes in the expected value and variance of the outcomes.
The Poisson distribution is related to the binomial setting, but looks at the number of successes over time instead of over many repeated trials.
Binomial random variables are a special type of variable that comes up we repeat a process for a set number of independent trials, with each trial having the same probability of success, and we count how many successes we get at the end of the trials. For example, how many shots will a basketball player make in a series of 10 shots?
Discrete_and_continuous_random_variables_and_probability_models
Discrete random variables occur in situations where we can list every possible outcome of a process, and assign each outcome a probability in a probability model. For example, how many heads will we get in three flips?
Expected value gives us the mean or long-term average number of success we can expect over time if we repeat a process over and over. For example, how much money can someone expect to win on average for each lottery ticket they buy?
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