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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.
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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
Importance and Application of probability: Importance of probability theory is in all those areas where event are not certain to take place as same as starting with games of
Histogram: It is generally used for charting continuous frequency distribution. In histogram, data are plotted as a series of rectangle one over the other. Class intervals
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In simple regression the dependent variable Y was assumed to be linearly related to a single variable X. In real life, however, we often find that a dependent variable may depend o
construction of control chart,n chart
What is a null hypothesis? ..
Using Chi Square Test when more than two Rows are Present To understand this, let us consider the contingency table shown below. It gives us the information about the stage
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
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