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The PCA is amongst the oldest of the multivariate statistical methods of data reduction. It is a technique for simplifying a dataset, by reducing multidimensional datasets to lower dimensions for analysis. It produces a small number of derived variables that are uncorrelated and that account for most of the variation in the original data set.'By reducing the number of variables'in this way, we can understand the underlying structure of the data. 'The derived variables are combinations of the original variables. For example, it might be that students take I0 examinations and some students do well in one examination while other students do better in another. It is difficult to compare one student with another when we have 10 marks to consider. One obvious way of comparing students is to calculate the mean score.
This is a constructed combination of the existing variables. However, one might get a more useful comparison of overall performances by considering other constructed cwbinations of the 10 exam marks. The PCA is one way of constructing such combinations, doing so in such a way as to account fer the maximum possible variation in the original data. We can then compare students' performance by considering this much smaller number of variables.
PCA states and then solves a well-defined statistical problem, and except for special cases always gives a unique solution wi.th some very nice mathematical properties. We can even describe some very artificial practical problems for which PCA provides the exact solution. The difficulty comes in trying to relate PCA to real-life scientific problems; the match is simply not very good. Actually PCA often provides a good approximation to common factor analysis, but that feature is now unimportant since both methods are now easy enough.
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The Neatee Eatee Hamburger Joint specializes in soyabean burgers. Customers arrive according to the following inter - arrival times between 11.00 am and 2.00 pm: Interval-arriva
Try different numbers of clusters in your program (K=2...15) and build a plot that shows the dependency between number K and value of RSS function on the last iteration. What is th
Use only the rare event rule, and make subjective estimates to determine whether events are likely. For example, if the claim is that a coin favors heads and sample results consis
PCA is a linear transformation that transforms the data to a new coordinate system such that the greatest variance by any projection of the data comes to lie on the first coordinat
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
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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
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Sample Standard Deviation So far, we discussed the population standard deviation. Now, let us switch to sample standard deviation(s) that is analogous to the population stand
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