Important Probability Distributions
General Continuous Distributions Recall that a continuous random variable or distribu-tion is defined via a probability density function Let f(x) (nonnegative) be the density function of variable X Then, f(x) is the rate at which probability accumulates in the neighborhood of x In other words, f(x)h ≈ P(x < X ≤ x +h)
Normal, Binomial, Poisson Distributions
3 Normal Distribution Applied to single variable continuous data e g heights of plants, weights of lambs, lengths of time Used to calculate the probability of occurrences less than, more than, between
Chapter 5: Discrete Probability Distributions
Chapter 5: Discrete Probability Distributions 158 This is a probability distribution since you have the x value and the probabilities that go with it, all of the probabilities are between zero and one, and the sum of all of the probabilities is one You can give a probability distribution in table form (as in table #5 1 1) or as a graph
Lecture: Probability Distributions
Lecture: Probability Distributions Probability Distributions random variable - a numerical description of the outcome of an experiment There are two types of random variables – (1) discrete random variables – can take on finite number or infinite sequence of values
Table of Common Distributions
Table of Common Distributions taken from Statistical Inference by Casella and Berger Discrete Distrbutions distribution pmf mean variance mgf/moment
Joint Distribution - Example - Duke University
Lecture 17: Joint Distributions Statistics 104 Colin Rundel March 26, 2012 Section 5 1 Joint Distributions of Discrete RVs Joint Distribution - Example Draw two socks at random, without replacement, from a drawer full of twelve colored socks: 6 black, 4 white, 2 purple Let B be the number of Black socks, W the number of White socks
MULTIVARIATE PROBABILITY DISTRIBUTIONS
MULTIVARIATE PROBABILITY DISTRIBUTIONS 3 Once the joint probability function has been determined for discrete random variables X 1 and X 2, calculating joint probabilities involving X 1 and X 2 is straightforward 2 3 Example 1 Roll a red die and a green die Let X 1 = number of dots on the red die X 2 = number of dots on the green die
Probability Distributions: Discrete vs Continuous
Just like variables, probability distributions can be classified as discrete or continuous Discrete Probability Distributions If a random variable is a discrete variable, its probability distribution is called a discrete probability distribution An example will make this clear Suppose you flip a coin two times
LES FONCTIONS DE LA DISTRIBUTION
Caractéristiques Exemples Le producteur est en contact avec la centrale d’achats d’un groupe de distri ution - Canal court intégré: la distribution est assurée par des groupes comme Carrefour, Auchan, FNAC - Canal court associé: la distribution est assurée par des groupes comme Leclerc 4
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