A statistical analysis performed to determine the origin of random data figures collected. Random factor analysis is used to decipher whether the outlying data is caused by an underlying trend or just simply random occurring events and attempts to explain the apparently random data. It uses multiple variables to more accurately interpret the data.

This data is used to help companies better focus their plans on the actual problem. If the random data is caused by an underlying trend or random norecurring event, that trend will need to be addressed and remedied accordingly. For example, consider a random event such as a volcano eruption. Sales of breathing masks may skyrocket, and if someone was just looking at the sales data over a multi-year period this would look like an outlier, but analysis would attribute this data to this random event.

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… 2. An Analysis of Eigenvectors of a Stock Market Cross-Correlation Matrix. Hieu T … Extending Risk Budgeting for Market Regimes and Quantile Factor Models. Emlyn James Flint and … Applications of random-matrix theory and nonparametric change-point analysis to three notable …

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… 2. An Analysis of Eigenvectors of a Stock Market Cross-Correlation Matrix. Hieu T … Extending Risk Budgeting for Market Regimes and Quantile Factor Models. Emlyn James Flint and … Applications of random-matrix theory and nonparametric change-point analysis to three notable …

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… 2. An Analysis of Eigenvectors of a Stock Market Cross-Correlation Matrix. Hieu T … Extending Risk Budgeting for Market Regimes and Quantile Factor Models. Emlyn James Flint and … Applications of random-matrix theory and nonparametric change-point analysis to three notable …

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… 2. An Analysis of Eigenvectors of a Stock Market Cross-Correlation Matrix. Hieu T … Extending Risk Budgeting for Market Regimes and Quantile Factor Models. Emlyn James Flint and … Applications of random-matrix theory and nonparametric change-point analysis to three notable …

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… 2. An Analysis of Eigenvectors of a Stock Market Cross-Correlation Matrix. Hieu T … Extending Risk Budgeting for Market Regimes and Quantile Factor Models. Emlyn James Flint and … Applications of random-matrix theory and nonparametric change-point analysis to three notable …

www.tandfonline.com [PDF]

… 2. An Analysis of Eigenvectors of a Stock Market Cross-Correlation Matrix. Hieu T … Extending Risk Budgeting for Market Regimes and Quantile Factor Models. Emlyn James Flint and … Applications of random-matrix theory and nonparametric change-point analysis to three notable …

www.tandfonline.com [PDF]

… 2. An Analysis of Eigenvectors of a Stock Market Cross-Correlation Matrix. Hieu T … Extending Risk Budgeting for Market Regimes and Quantile Factor Models. Emlyn James Flint and … Applications of random-matrix theory and nonparametric change-point analysis to three notable …

www.tandfonline.com [PDF]

… 2. An Analysis of Eigenvectors of a Stock Market Cross-Correlation Matrix. Hieu T … Extending Risk Budgeting for Market Regimes and Quantile Factor Models. Emlyn James Flint and … Applications of random-matrix theory and nonparametric change-point analysis to three notable …

www.annualreviews.org [PDF]

… 2. An Analysis of Eigenvectors of a Stock Market Cross-Correlation Matrix. Hieu T … Extending Risk Budgeting for Market Regimes and Quantile Factor Models. Emlyn James Flint and … Applications of random-matrix theory and nonparametric change-point analysis to three notable …

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Factor analysis is used to describe variability among observed variables in terms of a potentially lower number of unobserved (underlying) variables called factors.

By using factor analysis, we can reduce the amount of error that occurs within our data set.

Factor analysis searches for joint variations in response to unobserved latent variables.

It helps companies focus on actual problems.

Linear combinations are when you take one variable and multiply it by another variable.

To determine the origin of random data.

Companies use this analysis to help them better focus their plans.

Errors and residuals in statistics refers to the difference between what was expected from an experiment or study, and what actually happened.

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