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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 regression coefficients impossible. Including the sum of explanatory variables in the regression analysis would, for instance, lead to this problem. Estimated multi co linearity can also cause problems while estimating regression coefficients. In particular if multiple correlations for the regression of the particular explanatory variable on the others is high, then the variance of corresponding estimated regression coefficient will also be quite high.
t distribution
The theorem relating structure of the likelihood to the concept of the sufficient statistic. Officially the necessary and sufficient condition which a statistic S be sufficient for
Common cause failures (CCF): Simultaneous failures of the number of components due to a same reason. A reason can be external to the components, or it can be the single failure wh
Quasi-experiment is a term taken in use for studies which resemble experiments but are weak on some of the characteristics, particularly that allocation of the subjects to groups
This is given by common network e.g. Phone Company. The public networks are those networks, which are given by common carriers. It can be a telephone company or an other organizati
Catastrophe theory : A theory of how little is the continuous changes in the independent variables which can have unexpected, discontinuous effects on the dependent variables. Exam
The generalization of the normal distribution used for the characterization of functions. It is known as a Gaussian process because it has Gaussian distributed finite dimensional m
The Null Hypothesis - H0: There is no heteroscedasticity i.e. β 1 = 0 The Alternative Hypothesis - H1: There is heteroscedasticity i.e. β 1 0 Reject H0 if Q = ESS/2 >
Persson Rootze ´n estimator is an estimator for the parameters in the normal distribution when the sample is truncated so that all the observations under some fixed value C are re
Quality control procedures is the statistical process designed to ensure that the precision and accuracy of, for instance, a laboratory test, are maintained within the acceptable
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