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Negative hyper geometric distribution: In sampling without replacement from the population comprising of r elements of one kind and N - r of another, if two elements corresponding to which selected are replaced every time, then the probability of finding x elements of first kind in a random sample of n elements can be given as follows
The mean of distribution can be given as Nr/N and the variance can be given as follows (nr/N)(1-r/N)(N+n)/(N+1).
It corresponds to the beta binomial distribution with the integral parameter values.
Kurtosis: The extent to which the peak of the unimodal probability distribution or the frequency distribution departs from its shape of the normal distribution, by either being mo
Bonferroni correction : A procedure for guarding against the rise in the probability of a type I error when performing the multiple signi?cance tests. To maintain probability of a
The model which arises in the context of estimating the size of the closed population where individuals within the population could be identified only during some of the observatio
I have a problem I am trying to solve. An oil company thinks that there is a 60% chance that there is oil in the land they own. Before drilling they run a soil test. When there is
Confidence profile method : A Bayesian approach to meta-analysis in which the information in each piece of the evidence is captured in the likelihood function which is then used al
1) Consider an antenna with a pattern: G(θ,φ) = sinn(θ/θ0) cos(θ/θ0) where θ0 = Π/1.5 (a) What is the 3-dB bandwidth? (b) What is the 10-dB beam width? (c) What is t
Persson Rootze ´n estimator is an estimator for the parameters in the normal distribution when the sample is truncated so that all the observations under some fixed value C are re
Suppose we estimate the following model: Passengersi = 1 + 2Populationi + ui a) Generate a scatter plot with passengers on the vertical axis and population on the horizonta
how to get the proportional allocation of the give stratified random sampling example
Non linear mapping (NLM ) is a technique for obtaining a low-dimensional representation of the set of multivariate data, which operates by minimizing a function of the differences
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