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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.
Multitrait multi method model (MTMM) is the form of confirmatory factor analysis model in which the different techniques of measurement are used to measure each of the latent vari
MEANING ,IMPORTANCE AND RELEAVANCE OF SCATTER DIAGRAM
Formal graphical representation of the "causal diagrams" or the "path diagrams" where the relationships are directed but acyclic (that is no feedback relations allowed). Plays an
Population averaged models are the models for kind of clustered data in which the marginal expectation of response variable is the main focus of interest. An alternative approach
Mardia's multivariate normality test is a test that a set of the multivariate data arise from the multivariate normal distribution against departures due to the kurtosis. The test
Bioassay : It is an abbreviation of biological assay, which in its classical form includes an experiment conducted on biological material to determine relative potency of test and
The Null Hypothesis - H0: γ 1 = γ 2 = ... = 0 i.e. there is no heteroscedasticity in the model The Alternative Hypothesis - H1: at least one of the γ i 's are not equal
The Null Hypothesis - H0: There is no first order autocorrelation The Alternative Hypothesis - H1: There is first order autocorrelation Durbin-Watson statistic = 1.98307
The probability distribution which is a linear function of the number of component probability distributions. This type of distributions is used to model the populations thought to
Normal approximation : Normal distributions which approximate other distributions; such as, a normal distribution with the mean np and variance np(1 - p) which acts as an approxima
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