notes 21 - Continuous Random Variable Submitted by gfj100...

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Continuous Random Variable Submitted by gfj100 on Wed, 11/11/2009 - 10:42 Density Curves Previously we discussed discrete random variables, and now we consider the contuous type. A continuous random variable is such that all values (to any number of decimal places) within some interval are possible outcomes. A continuous random variable has an infinite number of possible values so we can't assign probabilities to each specific value. If we did, the total probability would be infinite, rather than 1, as it is supposed to be To describe probabilities for a continuous random variable, we use a probability density function. A probability density function is a curve such that the area under the curve within any interval of values along the horizontal gives the probability for that interval. Normal Random Variables The most commonly encountered type of continuous random variable is a normal random variable , which has a symmetric bell-shaped density function. The center point of the distribution is the mean value, denoted by μ (pronounced "mew"). The spread of the distribution is determined by the variance, denoted by σ 2 (pronounced "sigma squared") or by the square root of the variance called standard deviation, denoted by σ (pronounced "sigma"). Example
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This note was uploaded on 05/31/2011 for the course STAT 200 taught by Professor Andyregards during the Spring '11 term at World College.

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notes 21 - Continuous Random Variable Submitted by gfj100...

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