1 (PID 7.23.5) Recent trends show that the yield of your company's flagship product is declining. You are uncertain if the supplier of a key raw material is to blame, or if it is due to a change in your process conditions. You begin by investigating the raw material supplier. The data available has: = 24 = 6 + 1 designation of process outcome data set rawmaterialcharacterization Description: 3 of the 6 measurements are size values for the plastic pellets, while the other 3 are the outputs from thermogravimetric analysis (TGA), differential scanning calorimetry (DSC) and thermomechanical analysis (TMA), measured in a laboratory. These 6 measurements are thought to adequately characterize the raw material. Also provided is a designation Adequate or Poor that reflects the process engineer's opinion of the yield from that lot of materials. Import the data, and set the Outcome variable as a secondary identifier for each observation, as shown in the illustration below. The observation's primary identifier is its batch number. 1. Build a latent variable model for all observations and use autofit to determine the number of components. If your software does not have and autofit features (crossvalidation), then use a Pareto plot of the eigenvalues to decide on the number of components. 2. Interpret component 1, 2 and 3 separately (using the loadings bar plot). 3. Now plot the score plot for components 1 and 2, and colour code the score plot with the Outcome variable. Interpret why observations with Poor outcome are at their locations in the score plot (use a contribution plot). 4. What would be your recommendations to your manager to get more of your batches classified as Adequate rather than Poor? 5. Now build a model only on the observations marked as Adequate in the Outcome variable. 6. Reinterpret the loadings plot for 1 and 2. Is there a substantial difference between this new loadings plot and the previous one? Sheet1 Lot number B370 B880 B452 B287 B576 B914 B404 B694 B875 B475 B517 B296 B403 B430 B145 B319 B859 B990 B616 B133 B535 B745 B380 B986 Outcome Size5 Size10 Size15 TGA DSC TMA Adequate 13.8 9.2 41.2 787.3 18 65 Adequate 11.2 5.8 27.6 772.2 17.7 68.8 Adequate 9.9 5.8 28.3 602.3 18.3 50.7 Adequate 10.4 4 24.7 677.9 17.7 56.5 Adequate 12.3 9.3 22 593.5 19.5 52 Poor 13.7 7.8 27 597.9 18.1 49.8 Poor 15.5 10.7 34.3 668.5 19.6 55.7 Poor 15.4 10.7 35.9 602.8 19.2 53.6 Poor 14.9 11.3 41 614.6 18.5 50 Adequate 13.7 8.5 28 700.4 18 57 Poor 16.1 11.6 39.2 682.8 17.5 56.4 Adequate 12.8 5.4 23.7 739.4 18.2 59.8 Adequate 10.3 2.5 17.1 595.7 18.4 49.5 Poor 12.9 9.7 36.3 642.4 19.1 55 Adequate 13 7.3 27 682.8 19.1 55.3 Adequate 11.7 5.2 20.2 655.8 19.2 56.3 Adequate 10.7 6.8 27.7 661.2 18.3 55.5 Adequate 13 5.4 25.3 701.9 19.1 61 Adequate 11.9 7.2 29.8 661 18.5 55.4 Adequate 11.3 7.9 30 699.9 18.1 58.1 Adequate 11.1 4.5 24.8 576.3 19.5 51.6 Poor 10.2 5.8 24.7 575.9 18.5 46.2 Adequate 11.4 4.8 26.5 636 18.6 58.2 Adequate 10.7 4.8 26.5 726.7 19.2 58.4 Page 1