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