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The act of combining data from heterogeneous sources with the intent of extracting information that would not be available for any single source in isolation. An example is the combination of different satellite images to facilitate identification and tracking of objects. The term is also used to mean the combination of statistically heterogeneous samples to construct a new sample that can be regarded as having come from an unobserved joint distribution of interest that is the act of inferring a joint distribution when one has information only about the marginal distributions. See also record the linkage and copulas.
This term applied in the context of comparing the different methods and techniques of estimating the same parameter; the estimate with the lowest variance being regarded as the mos
How is the rejection region defined and how is that related to the z-score and the p value? When do you reject or fail to reject the null hypothesis? Why do you think statisticians
The model for data containing continuous and categorical variables both.The categorical data are summarized by the contingency table and their marginal distribution, 182by the mult
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
Zero-inflated Poisson regression is the model for count data with the excess zeros. It supposes that with probability p the only possible observation is 0 and with the probabilit
regression line drawn as Y=C+1075x, when x was 2, and y was 239, given that y intercept was 11. calculate the residual
Goodmanand kruskal measures of association is the measures of associations which are useful in the situation where two categorical variables cannot be supposed to be derived from
The Null Hypothesis - H0: There is autocorrelation The Alternative Hypothesis - H1: There is no autocorrelation Rejection Criteria: Reject H0 (n-s)R 2 > = (1515 - 4) x (0.
Perturbation theory : The theory useful in assessing how well a specific algorithm or the statistical model performs when the observations suffer less random changes. In very commo
Weighted least squares is the method of estimation in which the estimates arise from minimizing the weighted sum of squares of the differences between response variable and its pr
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