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Addition Rule for Mutually Exclusive(disjoint) Events
P(A or B) = P(A) + P(B)
Complement
The event that A does not occur.
Complement Rule
P(A^c) = 1 - P(A) where A^c is the complement of event A
Empirical Probability
The estimated probability of a specific outcome of a random process(like getting a heads when tossing a fair coin) obtained by actually performing many trials of the random process.
Event
Any collection of outcomes from some chance process.
General Addition Rule
If A and B are any two events resulting from some chance process, then P(A or B) = P(A) + P(B) - P(A and B).
Intersection
Of events A and B is the set of all outcomes in both events A and B.
Law of Large Numbers
If we observe more and more and more of any chance process, the proportions of times that a specific outcome occurs approaches its probability.
Mutually Exclusive(disjoint)
Events are this if they have no outcomes in common and so can never occur together - that is, if P(A and B) = 0.
Probability
Of any outcome of a chance process is a number between 0 and 1 that describes the proportion of times that a specific outcome would occur in a very long series of repetitions.
Probability Model
A description of some chance process that consists of two parts: a list of all possible outcomes called the sample space, and the probability for each outcome.
Random Process
Generates outcomes that are determined purely by chance.
Sample Space
A list of all possible outcomes in a probability model.
Simulation
The imitation of chance behavior, based on a model that accurately reflects the situation.
Union
Of events A and B is the set of all outcomes in either event A and B.
Venn Diagram
Consists of one or more circles surrounded by a rectangle. Each circle represents an event. The region inside the rectangle represents the sample space of the chance process.