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Unformatted text preview: c Kendra Kilmer October 27, 2011 Sections 8.5 and 8.6  The Normal Distribution Up until now, we have been dealing with finite discrete random variables. In finding the probability distribution, we could list the possible values in a table and represent it with a histogram. Definition : For a continuous random variable, a probability density function is defined to represent the proba bility distribution. Example 1 : Note that the for a continous random variable, X , P ( X ≤ x ) = P ( X < x ) Definition : We concentrate on a special class of continuous probability distributions known as normal distributions . Each normal distribution is defined by μ and σ . Each normal distribution has the follow ing characteristics: 1. The area under the curve is always 1. 2. The curve never crosses the x axis. 3. The peak occurs directly above μ 4. The curve is symmetric about a vertical line passing through the mean....
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This note was uploaded on 03/27/2012 for the course MATH 141 taught by Professor Jillzarestky during the Fall '08 term at Texas A&M.
 Fall '08
 JillZarestky
 Normal Distribution, Probability

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