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The values assigned to factors for the individual sample units in a factor analysis. The most common approach is "regression method". When the factors are seen as the random variables this corresponds to the best linear unbiased predictor and if the factors are supposed to have normal distributions to the empirical Bayes prediction. The Bartlett technique is also sometimes used which corresponds to the max imum likelihood estimation of factor scores if the factors are seen as ?xed.
A term commonly encountered in the application of the agglomerative hierarchical clustering techniques, where it refers to the 'tree-like' diagram illustrating the series of steps
Discuss the use of dummy variables in both multiple linear regression and non-linear regression. Give examples if possible
Link functions: The link function relates the linear predictor ηi to the expected value of the data. In classical linear models the mean and the linear predictor are identical
1) Has smartphones affected the consumer behavior? If so How ? And how is it going to change in future? 2) Forecasting of Mobile market (Time series analysis) 3) Comparison of fou
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
Chains of infection : The description of the course of infection among the group of individuals. The susceptibles infected by the direct contact with the introductory cases are sai
Hypothesis testing is a general term for procedure of assessing whether the sample data is consistent or otherwise with statements made about the population. It basically tells u
Interior analysis is the term now and again applied to analysis carried out on the fitted model in regression problem. The basic target of such analyses is the identification of
Hazard function : The risk which an individual experiences an event in a small time interval, given that the individual has survived up to the starting of the interval. It is th
what are all the organs
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