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Unformatted text preview: Section 8.5 – The Normal Distribution Section 8.6 – Application of the Normal Distribution A random variable that may take on infinitely many values is called a continuous random variable . A continuous probability distribution is defined by a function f called the probability density function . The probability that the random variable X associated with a given probability density function assumes a value in an interval a < x < b is given by the area of the region between the graph of f and the xaxis from x = a to x = b . The following graph is a picture of a normal curve and the shaded region is P(a < X < b) . Note: P( a < X < b ) = P( a < X < b ) = P( a < X < b ) = P( a < X < b ), since the area under one point is 0. The area of the region under the standard normal curve to the left of some value z, i.e. P (Z < z) or P (Z ≤ z) , is calculated for us in Table II, Appendix B on pg.1177. Section 8.5 – The Normal Distribution Section 8.6 – Applications of the Normal Distribution 1 Normal distributions have the following characteristics: 1. The graph is a bellshaped curve....
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 Fall '10
 AbdelnourAhmedZaid
 Math, Normal Distribution, Probability, 4 cm, 20 cm, 42%, 68.27%, 33 cm

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