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SOP01 Effectivity: Oct/25/20Edwin CaballeroAgosto Developing PL SDA Model in The UnscramblerXRevised:University of Puerto Rico at Mayaguez Revised by:Approved by:DEVELOPING PARTIAL LEAST SQUARE DISCRIMINANT
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How to fill out partial least squares for
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Standardize the data by mean centering and scaling the variables.
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Researchers and analysts in fields like chemometrics, genomics, and marketing who are interested in building predictive models with high collinearity among variables.
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Individuals working with large datasets where traditional regression methods may not be suitable due to multicollinearity.
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What is partial least squares for?
Partial least squares is a statistical method used for modeling the relationship between a set of independent variables and a dependent variable.
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Businesses or researchers who want to analyze data and identify relationships between variables typically use partial least squares.
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Partial least squares is typically filled out by inputting data into a statistical software program and running the analysis.
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The purpose of partial least squares is to find the underlying structure in the data by reducing the dimensions of the data set while still retaining the most relevant information.
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The data that needs to be reported on partial least squares includes the independent and dependent variables, as well as the results of the analysis.
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