| Prediction
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The following options are available in the
Prediction group box:
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| Use original scale
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Select this check box to generate predictions and residuals on the scale of the original or raw data. If you do not select it, predictions and residuals will be based on the normalized/scaled data.
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| Predictions
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Select this check box to produce a spreadsheet of predictions.
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| Residuals
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Click this button to display a spreadsheet of residuals. Residuals are defined as the deviations between the original variables and the predictions of the
TMPCA 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 indicate data abnormalities that cannot be sufficiently predicted by the model. Use
PCA's ability to detect outliers for process monitoring and quality control.
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| Scores
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Click this button to produce a spreadsheet of
t-scores, which are the representation of the original X variables in the new coordinate system, or the system of the principal components.
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| Loadings
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Click this button to create the scaled 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). Use loading factors to analyze the influence of the original variables in determining the
TMPCA model.
Note: The loading factors generated by clicking this button are actually multiplied (scaled) by the square root of their respective eigenvalues. Such scaling makes the comparison of the loading factors easier. In this representation, the loading factors are generally larger than those of the less important components.
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| Eigenvectors
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Click this button to generate a spreadsheet of eigenvalues for the
PC model.
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| Eigenvalues
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Click this option to generate a spreadsheet of eigenvalues for the
PC model.
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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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| Eigenvalues
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Following are the descriptions of the options in the
Eigenvalues group box.
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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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| D-To-Model
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Click this button to display 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
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Click this button to create the
D-To-Model in line plot format.
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 D-To-Model
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Click this button to create the
D-To-Model in histogram format.
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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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| Scaled data
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Click this button to generate a spreadsheet of the preprocessed variables X and Y. Preprocessing involves the application of a linear transformation which 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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