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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 coordinate (called the first principal component), the second greatest variance on the second coordinate, and so on. The PCA can be used for dimensionality reduction in a dataset while retaining those characteristics of the dataset that contribute most to its variance, by keeping lower-order principal components and ignoring higher-order ones. Such low-order components often contain the "most important" aspects of the data. But this is not necessarily the case, depending on the application. Let p and tn denote respectively the original and reduced number of variables. The original variables are denoted X. In the simplest case our measure of accuracy of reconstruction is the sum ofp squared multiple correlations between X-variables and the predictions of X made froin the factors. In the more general case we can weight each squared multiple correlation by the variance of the corresponding X-variable.
Since we can set those variances ourselves by multiplying scores on each variable,by any constant we choose, this amounts to the ability to assign any weights we choose to the different variables.
Grouped data For grouped data, the formula applied is σ = Where f = frequency of the variable, μ= population mea
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
Examine the given statement, then express the null hypothesis H0 and the alternative hypothesis H1 in symbolic form. The mean weight of women who won a beauty pageant is equal t
Disadvantages For calculating median it is necessary to arrange the data; other averages do not need any arrangement. Since it is a positional average, its value is not d
Standard Error The measure of reliability of the estimating equation that we have developed is given by standard error of estimate. The standard error of estimate represented b
Primary and Secondary Data: Primary Data: These data are those are collected for the first time. Thus primary data are original in character and gathered by actual observat
Applications of Standard Error Standard Error is used to test whether the difference between the sample statistic and the population parameter is significant or is d
how to interpret results, a good explanation to help me understand.
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