14111cn8.12-14

14111cn8.12-14 - c Kendra Kilmer Sections 8.5 and 8.6 The...

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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.

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14111cn8.12-14 - c Kendra Kilmer Sections 8.5 and 8.6 The...

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