| Coefficients
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Click this button to generate a spreadsheet of the
BMPLS coefficients. The coefficients of a
BMPLS model relate the u-scores to the t-scores.
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| Eigenvalues
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Click this button to generate a spreadsheet of eigenvalues for the PC model.
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| X Scores (t)
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Click this button to produce a spreadsheet of t-scores. The t-scores are the representation of the original X variables in the new coordinate system (the system of the principal components).
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| Loadings
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Click this button to create the matrix of the p-loading factors for the principal components in spreadsheet format. The loading factors determine the orientation of the principal component axes with respect to the original coordinate system (defined by X). Loading factors are used to analyze the influence of the original variables in determining the
BMPLS model.
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| X Weights
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Click this button to produce a spreadsheet containing the X variable weights.
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| Residuals
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Click this button to produce a spreadsheet of residuals. Residuals are defined as the deviations between the original variables and the predictions of the
BMPLS model. In other words, residuals are the unmodeled parts of the data that could not be matched by the predictions of the model. Large residuals are indications of abnormality in the data that cannot be sufficiently predicted by the model. The ability to detect outliers is a useful feature of
PCA that can be utilized for process monitoring and quality control.
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| Scaled data
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Click this button to generate a spreadsheet of the pre-processed variables X and Y. Preprocessing involves the application of a linear transformation that transforms the original data set to a new set of variables with zero mean and unit (or user specified) standard deviation.
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| Variable
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Use the options in this group box to generate data for variable trajectories in the plots and spreadsheet format.
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 Trajectory
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Click this button to generate a line plot of process variable trajectories against time for all batches in the data set.
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 (Trajectory Spreadsheet)
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Click this button to generate the same data in spreadsheet format.
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| Trajectory variable list
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Use the drop-down menu to select a variable for which you want to print a trajectory plot or spreadsheet.
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| Limits as regions
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Select this checkbox to display the area inside the control and warning limits of the variable trajectory chart as different colors in order to more easily identify the in-control and out-of-control batches.
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| Control
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In this box, specify the control limits. This value is multiplied by the computed standard deviation for defining the upper and lower limits.
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| Warning
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Select this check box to include an upper and a lower warning limit in the variable trajectory chart. Specify the warning limit in the adjacent box. This value will be multiplied by the computed standard deviation to define the lower and upper warning limits.
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| Scree plot
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Click this button to create an eigenvalue scree plot (Cattell, 1966) for the extracted principal components. By default only the extracted eigenvalues are included but you can extend this number (up to the maximum number of the eigenvalues) using
Number of eigenvalues option below.
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| Number of eigenvalues
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In this box, specify how many eigenvalues to be included in the scree plot.
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| Descriptives
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Click this button to produce a spreadsheet of various statistics of the original variables such as number of valid cases, means, standard deviations, and scale.
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| Y Scores (u)
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Click this button to produce a spreadsheet of u-scores. Scores are the representation of the original Y variables in the new coordinate system (the system of the principal components).
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| D-To-Model (X)
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Click this button to produce a spreadsheet of distance-to-model for all batches in the data set. Distance-to-model plays an important role in process control since it measures the squared perpendicular distance of an observation from the normal plane. Distance-to-model is used as an indication of whether a batch is within the domain of normality. Hence, it can be used for detecting outliers.
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 D-To-Model (X)
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Click this button to generate the
D-To-Model (X) in line plot format.
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 D-To-Model (X)
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Click this button to generate the
D-To-Model (X) in histogram format.
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