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Album: Public (HadleyMyers)

by Staff HadleyMyers
Figure 10: Columns panel, showing that most columns are categorical, with the exceptions of Mill Time-hr and Screen Size.
Figure 12: Binning dialog window, with cut point value changed to 70 and value labels changed to Reject and Accept.
Figure 11: Binning dialog window, with all default cut points selected and the remove button (-) indicated by a blue circle.
Figure 13: Distribution dialog window, with column Accept/Reject in the Y, Columns box.
Figure 15: Left-most three histograms of the Distribution report, which indicate embedded histograms for rejected batches.
Figure 14: Distribution report for column Accept/Reject.
Figure 18: Bivariate Fit of Dissolution vs Mill time-hr, with a linear fit and accompanying details indicating that the effect is likely real.
Figure 17: Dissolution vs Mill time-hr for all three values of Screen Size, with Lines of Fit depicting positive correlations for Sizes 3 and 5, but no correlation for Size 4.
Figure 16: Dissolution vs Mill time-hr with a Line of Fit depicting a positive correlation between the variables.
Figure 19: Oneway Analysis of Dissolution vs Screen Size, with a means comparison done using Student’s t-test. The results suggest a significant negative effect of using Screen Size 5.
Figure 20: Column Contributions from the Partition for Dissolution report, showing the significance of the factors in descending order. Spray Rate is seen to be the last significant factor; everything below it is considered not to have any effect on Dissolution.
Figure 21: Fit Model dialog window, with columns of significant factors selected.
Figure 22: Actual by Predicted Plot for a full response surface model with eight main effects, capturing 88% of the variation in Dissolution (RSq = 0.88).
Figure 24: Effect Summary for a refined RSM model containing only effects with a p-value greater than 0.05.
Figure 23: Effects Summary for a full response surface model with eight main effects.
Figure 25: Prediction Profiler for our refined RSM model.
Figure 27: Formula Depot with the Fit Least Squares model for Dissolution (left), with automatically generated Python code (right).
Figure 26: Prediction Profiler with Desirability Functions enables on, with factors at optimum settings resulting in highest value of Dissolution, 86.5% (y-axis adjusted from default).
Figure 29: Script window with the Run button near the top of the window circled in blue.
Figure 28: Log window, showing the Action Recording options within the red triangle menu, with only Data Table Operations selected.
Figure 30: Individual & Moving Range chart for both recipes, A (left) and B (right), with Limit Summaries providing the calculated means (Avg) and lower/upper control limits (LCL/UCL).
Figure 31: JMP Live new post configuration window, with the Enable Warnings box checked, for Control Chart warning emails. Toward the bottom, we can see that access to the report will be limited to the Engineering group.
Figure 32: JSL script window with the Control Chart Builder, now referenced by the name ccb.
Figure 33: JMP Public report with the report identifier code circled in blue.
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Figure 1: The JMP Analytic Workflow, showing all the tools needed to transform raw data into shareable insights for any organization.
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Figure 31: JMP Live new post configuration window, with the Enable Warnings box checked, for Control Chart warning emails. Toward the bottom, we can see that access to the report will be limited to the Engineering group.
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Added ‎11-30-2021 07:53 AM
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Figure 31: JMP Live new post configuration window, with the Enable Warnings box checked, for Control Chart warning emails. Toward the bottom, we can see that access to the report will be limited to the Engineering group.
Posted in: The JMP Analytic Workflow: A step-by-step example of how to use it to solve problems and create value for your organization - ( ‎12-13-2021 10:31 AM )

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