#How to use xlstat how to
#How to use xlstat software

However in certain areas, when the variables are supposed to be on an identical scale or we want the variance of the variables to influence factor building, covariance is used. Traditionally, a correlation coefficient rather than the covariance is used as using a correlation coefficient removes the effect of scale: thus a variable which varies between 0 and 1 does not weigh more in the projection than a variable varying between. Spearman, fully equivalent to a classic PCA (based on Pearson correlation) performed on the matrix of ranks.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.В 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.

#How to use xlstat software

Options for Principal Component Analysis in Excel using the XLSTAT software PCA on Pearson or Covariance?

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. The Principal Component Analysis, a Data Mining tool 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 itВ 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. PrincipalВ Component AnalysisВ is one of the most frequently used multivariate data analysis methods forВ dimensionalityВ reduction.

#How to use xlstat how to

We also provide many free learning resources on the web, such as a tutorial on how to run PCA in XLSTATВ as well as a guide to choose the bestВ data mining or multivariate data analysis method according to your situation.

Launch a pre-configuredВ Agglomerative Hierarchical Clustering (AHC) using theВ dendrogram button below the factor scores table.

Copy your PCA coordinates from the results reportВ to use them in further analyses.

Customize your correlation circle, your observations plot or your biplots as standard Excel charts.

Filter out variables or observations according to different criteria to optimize PCA map readability.

Add supplementary variables or observations.

Run your PCA on raw data or on dissimilarity matrices.

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. It is widely used in biostatistics, marketing, sociology, and many other fields. Principal Component Analysis ( PCA) is a powerful and popular multivariate analysis method that lets you investigate multidimensionalВ datasets with quantitative variables.