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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.
Quota sampling Under this method enumerators shall select the respondents in place of those not available, as per the quota fixed according to guide lines provided to them.
"index number is an economic barometer" comment on this statement
give me question on mean is the aimplest average to understand and easy to compute
This box plot displays the diversity wfood; the data ranges from 0.05710 being the minimum value and 0.78900 being the maximum value. The box plot is slightly positively skewed at
real time applications on graphical representation of o-give curves
A researcher computed the F ratio for a four-group experiment. The computed F is 4.86. The degrees of freedom are 3 for the numerator and 16 for the denominator. 1. Is the computed
The prevalence of undetected diabetes in a population to be screened is approximately 1.5% and it is assumed that 10,000 persons will be screened. The screening test will measure
The Null Hypothesis - H0: The random errors will be normally distributed The Alternative Hypothesis - H1: The random errors are not normally distributed Reject H0: when P-v
Theories of Business forecasting
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