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Negative binomial distribution is the probability distribution of number of failures, X, before the kth success in the sequence of Bernoulli trials where the probability of success at each trial is p and probability of failure is q =1-p. The distribution can be given as follows The mean, skewness, variance, and kurtosis of the distribution are as follows: Often used to model over the dispersion in count data.
A comprehensive regression analysis of the case study London has been carried out to test the 4 assumptions of regression: 1. Variables are normally distributed 2. Linear rel
A unified approach to all problems of prediction, estimation, and hypothesis testing. It is based on concept of the decision function, which tells the performer of experiment how t
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Behrens Fisher problem : The difficulty of testing for the equality of the means of the two normal distributions which do not have the equal variance. Various test statistics have
we are testing : Ho: µ=40 versus Ha: µ>40 (a= 0.01) Suppose that the test statistic is z0=2.75 based on a sample size of n=25. Assume that data are normal with mean mu and standa
Technically the multivariate analogue of the quasi-likelihood with the same feature that it leads to consistent inferences about the mean responses without needing specific supposi
Probability weighting is the procedure of attaching weights equal to inverse of the probability of being selected, to each respondent's record in the sample survey. These weights
Chapter 7 2. Describe the distribution of sample means (shape, expected value, and standard error) for samples of n =36 selected from a population with a mean of µ = 100 and a sta
Partial least squares is an alternative to the multiple regressions which, in spite of using the original q explanatory variables directly, constructs the new set of k regressor v
Length-biased sampling : The bias which arises in the sampling scheme based on the visits of patient, when some individuals are more likely to be chosen than others simply because
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