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Respondent-driven sampling (RDS): The form of snowball sampling which starts with the recruitment of the small number of people in the target population to serve as the seeds. After participating the seeds are asked to recruit other people they know in target population. The sampling continues in this manner with the current sample members recruiting the next wave of the sample members until the desired sample size is achived. By using the mathematical model which weights the sample to compensate for the fact that the sample was collected in a non-random manner, the data provided by such a sampling scheme can be used to give asymptotically unbiased estimates about target population. An instance of the use of this approach is the estimation of the drug user's in New York who have HIV.
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
Gllamm is a program which estimates the generalized linear latent and mixed models by the maximum likelihood. The models which can be fitted include structural equation models mul
Latent class analysis is a technique of assessing whether the set of observations including q categorical variables, in specific, binary variables, consists of the number of diffe
Ordination is the procedure of reducing the dimensionality (that is the number of variables) of multivariate data by deriving the small number of new variables which contain much
The approach to statistics based on a frequency view of probability in which it is supposed that it is possible to consider an in?nite sequence of the independent repetitions of th
This process of estimating from a data set those values lying beyond range of the data. In the regression analysis, for instance, a value of the response variable might be estimate
Conjoint analysis : The method used basically in market research which is similar in many respects to the various dimensional scaling. The method attempts to assign values to the l
The Null Hypothesis - H0: β 1 = 0 i.e. there is homoscedasticity errors and no heteroscedasticity exists The Alternative Hypothesis - H1: β 1 ≠ 0 i.e. there is no homoscedasti
moving and semi average method graphical reprsentation
how to resolve sequencing problem if jobs 6 given and 4 machines given. how to apply johnson rule for making to machines under this conditions. please give solution as soon as poss
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