Terms : Hide Images
| 11442794998 | probability | a number between 0 and 1 the describes the proportion of times the outcome would occur in a very long series of repetitions | 0 | |
| 11442794999 | law of large numbers | if we observe more and more repetitions of any chance process, the proportion of times that a specific outcome approaches a single value |  | 1 |
| 11442795000 | law of averages | do not mistake for law of large numbers -- idea that possible outcomes balance out in the future, i.e. getting heads on a coin flip six times in a row must be followed by getting tails six times; MYTH | 2 | |
| 11442795001 | simulation | an imitation of chance behavior based on a model that accurately reflects the situation Follows four-step process: State -- Ask a question of interest about some chance process. Plan -- Describe how to use a chance device to imitate one repetition of the process. Tell what you will record at the end of each repetition. Do -- Perform many repetitions of the simulation. Conclude -- Use the results of your simulation to answer the question of interest | 3 | |
| 11442795002 | sample space S | the set of all possible outcomes |  | 4 |
| 11442795003 | probability model | a description of some chance process that consists of two parts: a sample space S and probability for each outcome |  | 5 |
| 11442795004 | event | any collection of outcomes from some chance process; subset of sample space; usually designated by capital letters (ex. A, B, C, etc.) p(A0=(number of outcomes corresponding to event A)/(total number of outcomes in sample space) |  | 6 |
| 11443040695 | Rules of Probability | 1. The probability of any event must be between 0 and 1, inclusive. 0 ≤ P(E) ≤ 1. 2. The sum of the probabilities of all outcomes must equal 1. 3. If E and F are disjoint events, then P(E or F) = P(E) + P(F). If E and F are not disjoint events, then P(E or F) = P(E) + P(F) - P(E and F) 4. If E represents any event and Ec represents the complement of E, then P(Ec) = 1 - P(E) 5. If E and F are independent events, then P(E and F) = P(E)∗P(F) | 7 | |
| 11443073146 | basic probably rules | For any event A, 0 ≤ P(A) ≤ 1. If S is the sample space in a probability model, P(S) = 1. In the case of equally likely outcomes, use the P(A) formula Complement rule: P(AC) = 1 − P(A). Addition rule for mutually exclusive events: If A & B are mutually exclusive, P(A or B) = P(A) + P(B). |  | 8 |
| 11442795005 | mutually exclusive (disjoint) | two events that have no outcomes in common that can never occur together; when P(A and B) = 0 An example of a mutually exclusive event is flipping a coin. The result can be either heads or tails but never both, so it can be said that flipping a coin is mutually exclusive 1) Have no outcomes in common 2) Cannot be independent 3) Cannot occur at the same time 4) Have an intersection that is the "empty set" |  | 9 |
| 11442795006 | P(A^C) | Probability of NOT A within the sample space | 10 | |
| 11442795007 | Compliment rule | P(A^C) = 1 - P(A) not A | 11 | |
| 11442795008 | addition rule of mutually exclusive events | P(A or B) = P(A) + P(B), if A and B are mutually exclusive | 12 | |
| 11443104839 | intersection | shows A and B |  | 13 |
| 11442795009 | conditional probability | the probability that one event happens given that another event is already known to have happened; denoted by P(B|A) | 14 | |
| 11443121101 | conditional probability formula |  | 15 | |
| 11442795010 | general multiplication rule | P(A and B) = P(A ∩ B) = P(A) * P(B|A) where P(B|A) is the conditional probability that event B occurs given that A has already occured |  | 16 |
| 11442795011 | independent events | when the occurrence of one event does not change the probability that the other event will happen; if P(A|B) = P(A) and P(B|A) = P(B) two mutually exclusive events can never be independent because if one event happens, the other event is guaranteed not to happen (male and pregnant) 1) Cannot be disjoint 2) Means that the outcome of one event does not influence the outcome of any other event | 17 | |
| 11442795012 | multiplication rule for independent events | P(A ∩ B) = P(A) * P(B) if A and B are independent events, then the probability that A and B both occur | 18 | |
| 11442795013 | general addition rule | P(A or B) = P(A) + P(B) - P(A and B) if A and B are any 2 events resulting from some chance process | 19 | |
| 11442795014 | symbol for union | ∪ (means "or") | 20 | |
| 11442795015 | symbol for intersection | ∩ (means "and") | 21 | |
| 11444000513 | For events A and B related to the same chance process | If A and B are independent, then they cannot be mutually exclusive. these events are independent so they can't be mutually exclusive | 22 |
