Discrete Random Variables
: have outcomes that typically take on whole numbers as a result of
conducting an experiment; finite number.
Continuous random variables
: have outcomes that take on any numerical values as a result of
conducting an experiment; infinite number. Examples: time, distance, weight
o
The probability of a continuous random variable being a specific value is always
equal to zero
Expected Monetary Value:
is the mean of a discrete probability distribution when the discrete
random variable is expressed in term of value; it represents the long-term average profit for this
projects, as if this project occurred over and over again.
Types of Discrete Probability
1.
Binomial Distributions
a.
Can only have 2 possible outcomes
2.
Poisson Distributions
a.
The experiment consists of counting the number of occurrences of an event over
a period of time, area, distance, or any other types of measurement.
b.
The mean of Poisson distribution has to be the same for each equal interval of
measurement.
c.
The number of occurrences during one interval has to be independent of the
number of occurrences in any other interval
d.
The intervals that are defined in the Poisson process cannot overlap.

#### You've reached the end of your free preview.

Want to read all 5 pages?

- Fall '12
- Donnelly
- Normal Distribution, Probability theory, 68%, 84.2%, populationproportion