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As one of the oldest multivariate statistical methods of data reduction, Principal Component Analysis (PCA)simplifies a dataset by producing a small number of derived variables that are uncorrelated and that account for most of the variation in the original data set. Eventually, the derived variables are combinations of the original variables. For example, it might be ?hat students take 10 examinations and some students do well in one exam whilst other students do better in another. It is difficult to compare one student with another when we have marks from 10 examinations to consider. One obvious way of comparing students is to calculate tlie mean score. This is a constructed combination of the existing variables,. However. we may get a more useful comparison of overall performances by considering other constructed combinations of the 10 exam marks. The PCA is one way of constructing such combinations, doing so in such ewakas to account for as much as possible of the variation in the original data. One can then compare students' performance by considering this much sn~aller number of variables.
how to compute reliability coefficient for extracted factors in factor analysis?
Consider the linear transformation (a) Find the image of (3 , -2 , 2) under T. (b) Does the vector (5, 3) belong to the range of T? (c) Determine the matrix of the transf
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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Binomial Distribution Binomial distribution was discovered by swiss mathematician James Bernonulli, so this distribution is called as Bernoulli distribution also, this is a d
The Tastee Bakery Company supplies a bakery product to many supermarkets in a metropolitan area. The company wishes to study the effect of shelf display height employed by the supe
Examine properties of good average with reference to AM, GM, HM, MEAN MEDIAN MODE
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