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section4.1-4.2-students_SP11

section4.1-4.2-students_SP11 - Recap Discrete Distributions...

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1 Recap: Discrete Distributions section 3.6 1 Hypergeometric ( ) r N r x n x P X x N n , max{0, ( )}, ,min{ , } x n N r n r 1 ( ) (1 ) 1 x r r x P X x p p r , x = r , r +1, … 2 (1 ) ( ) ( ) r r p E X V X p p Negative Binomial ( ) , where / . ( ) 1 E X np p r N Var X fpc n p p 2 What is ahead: Continuous Distributions Generic setup for continuous distributions: Cumulative Distribution Function ( cdf ): F(x) = P(X ≤ x) Probability Density Function ( pdf ) : f(x) “similar” to pmf P (X ≤ b), P(X ≥ a), P(a ≤ X ≤ b), …. Expectation : E[X] = μ Variance : V(X) = E [ (X - μ) 2 ] Specific Continuous Distribution Uniform Normal (Gaussian) Gamma, Exponential, Chi- squared, …
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