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Random Factor Analysis

What is 'Random Factor Analysis'

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.

Explaining 'Random Factor Analysis'

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.


Further Reading


Random matrix theory and financial correlations
www.worldscientific.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 …

Generating composite volatility forecasts with random factor betasGenerating composite volatility forecasts with random factor betas
www.sciencedirect.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 …

Attitudes to money in a random sample of adults: Factor analysis of the MAS and MBBS scales, and correlations with demographic variablesAttitudes to money in a random sample of adults: Factor analysis of the MAS and MBBS scales, and correlations with demographic variables
www.sciencedirect.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 …

Experiments with more than one random factor: Designs, analytic models, and statistical powerExperiments with more than one random factor: Designs, analytic models, and statistical power
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 …

Analysis of means approach for random factor analysisAnalysis of means approach for random factor analysis
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 …

Exploratory factor analysis with small sample sizesExploratory factor analysis with small sample sizes
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 …

Entrepreneurs in Turkey: A factor analysis of motivations, success factors, and problemsEntrepreneurs in Turkey: A factor analysis of motivations, success factors, and problems
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 …

The three types of factor models: A comparison of their explanatory powerThe three types of factor models: A comparison of their explanatory power
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 …

Exploratory structural equation modeling: An integration of the best features of exploratory and confirmatory factor analysisExploratory structural equation modeling: An integration of the best features of exploratory and confirmatory factor analysis
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 …



Q&A About Random Factor Analysis


What is the purpose of factor analysis?

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

How can errors be reduced with factor analysis?

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

How does factor analysis find underlying factors?

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

What does it do?

It helps companies focus on actual problems.

What are linear combinations?

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

What is the purpose of random factor analysis?

To determine the origin of random data.

Who uses this analysis?

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

What are errors and residuals in statistics?

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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