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MSIS 3223 - EXAM 2/QUESTIONS AND ANSWERS A+ GRADED
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MSIS 3223 - EXAM 2/QUESTIONS AND ANSWERS A+ GRADED
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MSIS 3223 - EXAM 2/QUESTIONS ANDANSWERS A+ GRADED
Probability - -The likelihood that an outcome occurs
- Experiment - -The process that results in an outcome
- Outcome - -Result of an experiment that can be observed
- Sample space - -The collection of all possible outcomes of an experiment
- Event - -A collection of one or more outcomes from a sample space
- Complement of an event - -All possible outcomes that are not in the event
- Union of events - -If the notation (A and B) represents the intersection of
events A and B, the collection of all outcomes that belong to either A or B or
to both A and B
- Non-mutually exclusive events - -If the notation (A and B) represents the
intersection of events A and B, the collection of all events that belong to both
A and B
- Combinations - -Unique outcomes that must be counted when order does
not matter
- Permutations - -The outcomes that are counted when order does matter
- Classical definition of probability - -If the process that generates the
outcome is known, probabilities can be deduced from theoretical arguments
- Relative frequency definition of probability - -Probabilities are based on
empirical data; the probability is simply the relative frequency associated
with that outcome
- Subjective definition of probability - -Probabilities are based on judgment
and experience
- Joint probability - -The probability of the intersection of two events
- Marginal probability - -The probability of an event irrespective of the
outcome of the other joint event
, - Conditional probability - -The probability of occurrence of one event A,
given that another event B is known to be true or has already occurred
- Independent events - -Two or more events in which the outcome of one
event does not affect the outcome of the other event(s)
- Random variable - -A numerical description of the outcome of an
experiment
- Discrete random variable - -Variable where the number of outcomes can
be counted and each outcome has a measurable and positive probability
- Continuous random variable - -A random variable that may have outcomes
over one or more continuous intervals or real numbers
- Probability distribution - -A characterization of the possible values that a
random variable may assume along with the probability of assuming these
values
- Empirical probability distributions - -Probability distribution calculated
from the relative frequencies of a data sample
- Subjective distribution - -Probability distribution specified from subjective
values and expert judgment by creating decision models for phenomena for
which there is no historical data
- Discrete distributions - -Probability distribution of the discrete outcomes of
a discrete random variable
- Cumulative distribution function - -A function that specifies the probability
that a random variable is less than or equal to a specified value
- Expected value - -The average of each possible outcome of a future event,
weighted by its probability of occurring
- Variance of a random variable - -The weighted average of the squared
deviation of the values of the variable X from its mean
- Probability density function - -The function that describes the probability
distribution of a continuous random variable
- Standard normal distribution - -A normal distribution with a mean of 0 and
a standard deviation of 1.
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