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Poisson regression
In case of Poisson regression we use ηi = g(µi) = log(µi) and a variance V ar(Yi) = φµi. The case φ = 1 corresponds to standard Poisson model. Poisson regression is used when the response to model is counts which typically follow a Poisson distribution. Examples include colony counts for bacteria or viruses, accidents, equipment failures, insurance claims, incidence of disease. Interest often lies in estimating a rate of incidence and determining its relationship to a set of explanatory variables. Again, an IRLS procedure is used to ?nd the MLE estimators of the β coeffcients. When we can not assume φ = 1, (this is the case of over- or under- dispersion discussed in McCullagh and Nelder (1989)), the iterative procedure is changed to so called "quasi-likelihood estimation". Finally in this section, we shall also mention shortly the extension of GLM to GAM.
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Meta-analysis is the collection of techniques whereby the results of two or more independent studies are statistically combined to yield the overall answer to a question of intere
Lie factor : A measure suggested by Tufte for judging the honesty of the graphical presentation of data. Which can be calculated as follows The values close to one are desir
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how to constuct design matrix
The model which is applicable to the longitudinal data in which the dropout process might give rise to the informative lost values. Specifically if the study protocol specifies the
Biplots: It is the multivariate analogue of the scatter plots, which estimates the multivariate distribution of the sample in a few dimensions, typically two and superimpose on th
Zero-inflated Poisson regression is the model for count data with the excess zeros. It supposes that with probability p the only possible observation is 0 and with the probabilit
The special cases of the probability distributions in which the random variable's distribution is concentrated at one point only. For instance, a discrete uniform distribution when
An analyst counted 17 A/B runs and 26 time series observations. Do these results suggest that the data are nonrandom? Explain
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