Stat400Lec9(Ch3.3) - Stat 400 Lecture 9 Spring 2012...

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Stat 400 Lecture 9 Spring 2012 Review (2.6) Poisson Processes, Poisson distribution. Mean, variance and m.g.f. of Poisson distribution. Poisson Approximation to Binomial distribution. Today’s Lecture (3.3) Continuous random variable and its c.d.f. Probability density function (p.d.f.), its properties. Mean, variance, moment generating function Percentiles for p.d.f. Comparison between discrete and continuous random variables 1 of 11
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Spring 2012 Continuous-type random variable and its c.d.f. The random variable X is a continuous random variable if X takes all values in an interval of numbers. Wheel of fortune: the angle of the pointer is within [0 o , 360 o ) . The length of time it takes to check out at Walmart in the weekend. The time we wait to see the next volcano eruption in the world. Note: When X is continuous, P ( X = x ) = 0 for all x . The probability mass function is meaningless. Although we cannot assign a probability to any value of X , we are able to assign probabilities to intervals: e.g. P ( X = 1) = 0 , but P (0 . 999 X 1 . 001) can be > 0 . 2 of 11
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This note was uploaded on 03/14/2012 for the course STAT 400 taught by Professor Kim during the Spring '08 term at University of Illinois, Urbana Champaign.

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Stat400Lec9(Ch3.3) - Stat 400 Lecture 9 Spring 2012...

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