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Henry Kaiser suggested a rule for selecting a number of components m less than the number needed for perfect reconstruction: set m equal to the number of eigenvalues greater than I. This rule is often used in common factor analysis as well as in PCA. Several lines of thought lead to Kaiser's rule, but the simplest is that since an eigenvalue is the amount of variance explained by one more component, it doesn't make sense to add a component that explains less variance than is contained in one variable. Since a component analysis is supposed to summarize a set of data, to use a component that explains less than a variance of I is something like writing a summary'of a book in which one section of the summary is longer than the book sectio~it summarizes--which makes no sense. However, Kaiser's ma-jor justification for th5 rule was that it matched pretty well the ultimate rule of doing several component analyses with diff-nt- numbers of komponents, and seeing which analysis made sense. That ultimate rule is much easier today than it was a generation ago, so Kaiser's rule seems obsolete.
differance b/w big M mathod and two phase mathod
(a) Elevation (m) 0 400 800 1200 1600 2000 2400 2800 3200 4000 480
Under the standard cost method which is also referred as the standard cost method ,stock receipts are assigned a standard cost. Any variations between the actual cost and standard
prove that coefficient of correlation lies between -1 and+1
Methods of Forecasting Various techniques which are generally used in business forecasting are as under: 1. Forecasting through the opinion of heads of department
construction of control chart,n chart
Analysis of variance allows us to test whether the differences among more than two sample means are significant or not. This technique overcomes the drawback of the method used in
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what is the aim of statistics?
Features of index numbers
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