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Principal components analysis is a process for analysing multivariate data which transforms original variables into the new ones which are uncorrelated and account for decreasing the proportions of variance in the data. The goal of the method is to decrease the dimensionality of the data. The principal components, new variables, are defined as the linear functions of the usual variables. If the first few principal components account for the large percentage of the variance of the observations (say it above 70%) they can be used both to simplify subsequent analyses and to display and summarize the data in a parsimonious way.
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
The model which arises in the context of estimating the size of the closed population where individuals within the population could be identified only during some of the observatio
Remedian: The robust estimator of location which is computed by an iterative process. By assuming that the sample size n can be written as bk where b and k are the integers, the s
Intention-to-treat analysis is the process in which all the patients randomly allocated to a treatment in the clinical trial are analyzed together as representing that particular
The statistical methods for estimation and inference which are based on a function of sample observations, probability distribution of which does not rely upon a complete speci?cat
This is the theorem which states that if the error terms in a multiple regression have the same variance and are not corrected, then the estimators of the parameters in the model p
Discuss the use of dummy variables in both multiple linear regression and non-linear regression. Give examples if possible
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
when there is tie in sequencing then what we do
Multi co linearity is the term used in the regression analysis to indicate situations where the explanatory variables are related by a linear function, making the inference of the
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