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Covariance, that works on unstandardized variances and covariances (variables with high variances will play stronger roles in the outputs.

Pearson, the classic PCA, that automatically standardizes or normalizes the data prior to computations to avoid inflating the impact of variables with high variances on the result.

XLSTAT offers several data treatments to be used on the input data prior to Principal Component Analysis computations: It is common to use the Pearson correlation coefficient or the covariance as the index of similarity, Pearson correlation and covariance have the advantage of giving positive semi-defined matrices whose properties are used in PCA. However other indexes may be used. PCA is used to calculate matrices to project the variables in a new space using a new matrix which shows the degree of similarity between the variables.

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How to configure a Principal Component Analysis in XLSTAT? PCA on Pearson or Covariance XLSTAT proposes several standard and advanced options that will let you gain a deep insight into your data. XLSTAT provides a complete and flexible PCA feature to explore your data directly in Excel. Visualizing observations in a 2- or 3-dimensional space in order to identify uniform or atypical groups of observations.

Obtaining non-correlated factors which are linear combinations of the initial variables so as to use these factors in modeling methods such as linear regression, logistic regression or discriminant analysis.The study and visualization of the correlations between variables to hopefully be able to limit the number of variables to be measured afterwards.There are several uses for it, including: PCA can thus be considered as a Data Mining method as it allows to easily extract information from large datasets. If the information associated with the first 2 or 3 axes represents a sufficient percentage of the total variability of the scatter plot, the observations could be represented on a 2 or 3-dimensional chart, thus making interpretation much easier. PCA dimensions are also called axes or Factors. It is a projection method as it projects observations from a p-dimensional space with p variables to a k-dimensional space (where k < p) so as to conserve the maximum amount of information (information is measured here through the total variance of the dataset) from the initial dimensions. It is widely used in biostatistics, marketing, sociology, and many other fields. Principal Component Analysis is one of the most frequently used multivariate data analysis methods that lets you investigate multidimensional datasets with quantitative variables. What is principal component analysis? Definition of a Principal Component Analysis

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