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exam p distributions

Nguyen Tran Mathematics Englisch 12 Karten

exam p distributions

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  1. 1Discrete UniformEqual probability of each outcome
    X ~ DUnif(a,b)
    f(x) = 1 / (b-a+1)
    E[X] = (a+b) / 2
    VAR[X] = (b-a+1)^2 - 1 / 12
    Discrete Uniform
  2. 2Binomialbin(n,p)
    to # count successes of n independent trials with constant success probability p and binary outcomes for each trial
    Binomial
  3. 3hypergeometricf(x) above is for two categories
    hypgeom(M,N,n)
    to count # successes of n dependent trials with P(success) not constant across the trials (sampling w/o replacement)
    hypergeometric
  4. 4geometricgeom(p)
    to count # independent bernoulli trials required to get 1 success where each trial has p = P(success)
    geometric
  5. 5negative binomialnegbin(r,p)
    how many independent bernoulli trials until r successes
    negative binomial
  6. 6poissonpois(lambda)
    to count # occurrences of an event in a fixed interval
    poisson
  7. 7continuous uniformeg. a bus arrives at a bus stop uniformlycontinuous uniform
  8. 8exponentialexp(theta=1/lambda)
    to model waiting time between independent events that occur with a constant average rate (lambda)
    exponential
  9. 9gammagamma(alpha,beta)
    to model waiting until next alpha occurrences of an event
    gamma
  10. 10normalnorm(mu, sigma2)
    standard normal rv Z ~ norm(0,1)
    normal
  11. 11lognormalX ~ lognorm <=> ln(X) ~ norm
    Y ~ norm <=> e^Y ~ lognorm
    lognormal
  12. 12betabeta(alpha beta)
    to model probabilities
    beta

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Beschreibung
pmfs,pdfs, expected values, variances, special properties
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Nguyen Tran
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