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- StudyBlue
- Iowa
- Iowa State University
- Statistics
- Statistics 104
- Joseph
- Chapter 4 Vocab

Betsy S.

Probability

a measure of the likelihood of a random phenomenon or chance behavior

Experiment (probability definition)

any process that can be repeated in which the results are uncertain

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Law of Large Numbers

as the number of repetitions of a probability experiment increases, the proportion with which a certain outcome is observed gets closer to the probability of the outcome

Simple Event

any single outcome from a probability experiment

Sample Space, S

the collection of all possible outcomes or simple events

Event

any collection of outcomes from a probability experiment

Probability of an Event

the likelihood of that event occuring

Theoretical Approach

the theoretical method of computing probabilities requires equally likely outcomes; each simple event has the same probability of occuring

P(E)

N(E) / N(S)

Empirical/Experimental Approach

probabilities are obtained from empirical evidence; evidence based upon the outcomes of a probability experiment

Relative Frequency of E

frequency of E / number of trials of the experiment

Subjective Probabilities

probabilities obtained based upon an educated guess or personal opinion

Compound Events

formed by combining two or more events

Addition Rule

P(E or F) = P(E) + P(F) - P(E and F)

Mutually Exclusive

if events E and F have no simple events in common or cannot occur simultaneously

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Addition Rule for Mutually Exclusive Events

P(E or F) = P(E) + P(F)

Complement of an Event

all simple events in the sample space, S, that are not simple events in the event E

Complement Rule

P(Ec) = 1 - P(E)

Conditional Probability

the probability of an event F occurring given the occurrence of event E

Multiplication Rule

P (E and F) = P(E) * P(F|E)

Joint Probability

P (E and F)

Marginal Probability

P(E)

Independent

the occurrence of event E in a probability experiment does not affect the probability of an event F

Dependent

the occurrence of event E in a probability experiment affects the probability of event F

Independent Events

P(F|E) = P(F) or P(E|F) = P(E)

Conditional Probability Rule

P(F|E) = P(E and F) / F(E)

Bayes Rule

P(E|F) * P(F) = P(F|E) * P(E)

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