A sample point refers to a(n)
individual outcome of an experiment
The set of all possible sample points (experimental outcomes) is called
the sample space
A graphical device used for enumerating sample points in a multiple-step experiment is a
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When the results of experimentation or historical data are used to assign probability values, the method used to assign probabilities is referred to as the
relative frequency method
A graphical method of representing the sample points of a multiple-step experiment is
a tree diagram
Given that event E has a probability of .25, the probability of the complement of event E
must be 0.75
The union of events A and B is the event containing
all the sample points belong to A or B or both
Probability
A numerical measure of the likelihood than an event will occur
Experiment
A process that generates well-defined outcomes
Sample Space
the set of all experimental outcomes
Sample point
An element of the sample space. A sample point represents an experiment outcome.
Tree diagram
A graphical representation that helps in visualizing a multiple-step experiment
Classical method
A method of assigning probabilites that is appropriate when all experimental outcomes are equally likely
Relative frequency method
A method of assigning probabilities that is appropriate when data are available to estimate the proportion of the time the experimental outcome will occur if the experiment is repeated a large number of times
Subjective method
A method of assigning probabilities on the basis of judgment
Event
A collectiion of sample points.
Complement of A
The event consisting of all sample points that are not in A
Venn diagram
A graphical representation for showing symbolically the sample space and operatons involving events in which the sample space is represented by a rectangle and events are represented as circles within the sample space
Union of A and B
The event containing all sample points belonging to A or B or both. The union is denoted A U B.
Intersection of A and B
The event containing the sample points belonging to both A and B. The intersection is denoted A n B.
Addition law
A probability law used to compute the probability of the union of two events. It is P(A U B) = P(A) + P(B) - P(A n b). For mutually exclusive events, P(A n B) = 0; in this case the addition law reduces to P(A U B) = P(A) + P(B).
Mutually Exclusive Events
Events that have no sample points in common; that is, A n B is empty and P(A n B) = 0.
Conditional Probability
The probability of an event given that another event ialready occured. The conditional probability of A given B is P(A | B) = P(A n B)/P(B)
Joint Probability
The probability of two events both occurring; that is, the probability of the intersection of two events.
Marginal Probability
The values in the margins of a joint probability table that provide the probabilities of each event separately.
Independent events
Two events A and B where P(A | B) = P(A) or P(B | A) = P(B); that is, the events have no influence on each other
Multiplication Law
A brabilitiy law used to compute the probability of the itnersection of two events. It is P(A n b) - P(B)P(A | B) or P(A n B) = P(A)P(B | A). For independent events it reduces to P(A n B) = P(A)P(B).
Prior Probabilities
Initial estimates of the probabilities of events
Posterior Probabilities
Revised probabilities of events based on additional information.
Bayes'theorem
A method used to compute posterior probabilities.