- 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

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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

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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