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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.
First we look at these charts assuming that we know both the mean and the standard deviation of the process, that is μ and σ . These values represent the acceptable values (bench
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Median Median is a position average. It is the value of middle item of a variable when the items are arranged according to their values either in ascending or descending order.
Index Number Meaning and Definitions of Index Number The index numbers are the special type of averages which are presented in percentages and computed on certain base.
Explanation of standard deviation and variance Describe the importance of standard deviation and variance, what they calculate and why they are required. Importance of char
difference between large sample test and small sample test
Standard Deviation The main drawback of the deviation measures of dispersion, as discussed earlier, is that the positive and negative deviations cancel out each other. Use of t
Examples of grouped, simple and frequency distribution data
An approximation to the error of a Riemannian sum: where V g (a; b) is the total variation of g on [a, b] dened by the sup over all partitions on [a, b], including (a; b
The displacement of a simply supported beam subject to a uniform load is given by the solution of the following differential equation (for small displacements); and q is th
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