Complimentary events
Two events that taken together include all the outcomes for an experiment but do not contain any common outcome
Compound event
An event that contains more than one outcome of an experiment
Conditional probability
The probability of an event subject to the condition that another event has already occurred
Dependent events
Two events for which the occurrence of one changes the probability of the occurrence of the other
Equally likely outcomes
Two or more outcomes or events that have the same probability of occurrence
Event
A collection of one or more outcomes of an experiment
Experiment
A process with well defined outcomes that, when performed, results on one and only one of the outcomes per repetition
Impossible event
An event that cannot occur
Independent events
Two events for which the occurrence of one does not change the probability of the occurrence of the other
Joint probability
The probability that two or more events occur together
Marginal probability
The probability of one event or characteristic without consideration of any other event
Mutually exclusive events
Two or more events that do not contain any common outcome and, hence, cannot occur together
Outcome
The result of the performance of any experiment
Probability
A numerical measure of the likelihood that a specific event will occur
Sample space
the collection of all (sample points or) outcomes of an experiment
Subjective probability
The probability assigned to an event based on the information and judgement of a person
Sure event
An event that is certain to occur
Random variable
A variable whose value is determined by the outcome of a random experiment
Discrete random variable
A random variable whose values are countable (whole number)
Continuous random variable
A random variable that can assume any value in one or more intervals (decimals)
If two events A and B are independent, then
P(A)=P(A|B)
If two events A and B are complementary, then
P(A)=1-P(B) because P(A)+P(B)=1
If two events A and B are mutually exclusive, then
P(A or B)=P(A)+P(B)
Or
Add
And
Multiply
Conditions for z
m=0 and �=1
Greater than is to the
Right
Less than is to the
Left
If greater than, right area =
1-left area
A normal distribution has 3 characteristics
1. The center of the curve is m (the curve is symmetric about the mean).
2. The tails go on indefinitely.
3. The total area under the curve is 100% or 1.
Z identifies a
STANDARD normal distribution
Joint probability indicated by
n & and
Probability of union is indicated by
U & or