The ratings follow a 0 – 20 scale, where 0 is the worst and 20 is the best. Rating, the variable consisting of the experts’ ratings, is the response of interest. You want to identify the wine-related factors that maximize the response.
Because each rater tastes eight wines, Rater is a blocking factor with eight runs per block. For this experiment, only these five raters are of concern. You are not interested in generalizing to a larger population of raters.
You have identified nine process factors for the study. These include the grape variety, the field on which the grapes were grown, and seven other factors related to processing. You can experiment with any combination of these factors. Also, the factors can be varied at will as part of the experiment. Relative to the experiment, these factors are all “Easy” to change. For information about specifying factor changes, see Changes and Random Blocks.
The factors and their levels appear in Process Factors and Levels for Wine Tasting Experiment. Note that all of these factors are categorical. The factors and their levels are also given in the factor table Wine Factors.jmp in the Design Experiment folder of Sample Data.
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For information about the complete DOE workflow, see The DOE Workflow: Describe, Specify, Design in Starting Out with DOE.
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Select DOE > Custom Design.
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Completed Responses and Factors Outlines shows the completed Responses outline.
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Note that Role is set to Blocking. Note also that only one setting for Values appears. This is because the number of blocks cannot be determined until the desired number of runs is specified. Once you specify the Number of Runs in the Design Generation outline, the number of levels for Rater updates to what is required.
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Click Add Factor > Categorical > 4 Level.
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Yeast (Cultured and Wild)
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Temperature (High and Low)
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Press (Hard and Soft)
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Barrel Age (New and Two Years)
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Barrel Seasoning (Air and Kiln)
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Filtering (No and Yes)
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Click Continue.
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From the Custom Design red triangle menu, select Load Factors.
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The Model outline shows all main effects as Necessary, indicating that the design needs to be capable of estimating all main effects. For this example, your assumed model reflects your interest in main effects only. However, if you wanted to estimate other effects, you could add them to the Model outline. See Model.
The Alias Terms outline specifies the effects to be shown in the Alias Matrix, which appears later. See Alias Matrix. The Alias Matrix shows the aliasing relationships between the Model terms and the effects listed in the Alias Terms outline. Open the Alias Terms outline node to verify that all two-factor interactions are listed.
In the next step, you generate your design. Because the Custom Design algorithm begins with a random starting design, your design might differ from the one shown in Design for Wine Experiment. If you want to obtain a design with exactly the same runs and run order, perform the following steps:
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From the Custom Design red triangle menu, select Set Random Seed.
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Click OK.
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From the Custom Design red triangle menu, select Number of Starts.
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Click OK.
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Click Make Design.
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The only red in Color Map on Correlations is on the main diagonal. The color indicates absolute correlations of one, reflecting that each term is perfectly correlated with itself. It follows that no main effect is completely confounded with any two-way interaction. In fact, the absolute values of the correlations of main effects with two-way interactions are fairly low. This means that estimates of main effects might be only slightly biased by the presence of active two-way interactions.
For example, consider the model effect Barrel Seasoning. If Variety*Press is active, then the expected value of the estimate for the Barrel Seasoning effect differs from an unbiased estimate of that effect. The amount by which it differs is equal to 0.4 times the effect of Variety*Press. Therefore, what appears to be a significant Barrel Seasoning estimated effect could in reality be a significant Variety*Press effect.
The Design Diagnostics outline provides information about the efficiency of the design. Efficiency measures compare your design to a theoretically optimal design, which might not exist. The efficiency values are ratios, expressed as percents, of the efficiency of your design to the efficiency of this optimal design. For details about the efficiency measures, see Estimation Efficiency in Evaluate Designs.
The first line in the Design Diagnostics outline indicates that your design was constructed to optimize the D-efficiency criterion. For more details, see the Optimality Criterion description in Custom Design Options. In this case, your design has D Efficiency of 100%.
Specify the order of runs in your data table using the Output Options panel. The default selection, Randomize within Blocks, is appropriate for this example. Simply click Make Table.
Note: Your table might look different because the algorithm that creates it uses a random starting design. To obtain the precise table shown in Custom Design Table, see Design.
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In the Table panel, the Model script and the DOE Dialog script are added during the design creation process. The Model script opens a Fit Model window containing the effects that you specified as Necessary in the Custom Design dialog. The DOE Dialog script re-creates the window used to generate the design table.
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Now you are ready to run your experiment, gather the Rating data, and insert the results in the Rating column of your Custom Design table.
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The Wine Data.jmp table is exactly the same as the Custom Design table shown in Custom Design Table, except that it contains your recorded experimental results.
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Notice that Rater, the blocking factor, is added as a fixed effect, rather than as a random block effect. This is appropriate because the five raters were specifically chosen and are not a random sample from a larger population.
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Click Run.
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The Effect Tests report indicates that seven of the model terms are significant at the 0.05 level. Field, Temperature, and Barrel Age are not significant.
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In the Effect Summary report, press the Control key and hold it as you select Temperature, Field, and Barrel Age.
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Click Remove.
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Profiler for Reduced Model shows the Prediction Profiler. Recall that you specified a response goal of Maximize, with lower and upper limits of 0 and 20. Setting these limits caused a Response Limits column property to be saved to the Rating column in the Custom Design table. The Prediction Profiler uses the Response Limits information to construct a Desirability function, which appears in the right-most plot in the top row in Profiler for Reduced Model. The bottom row displays Desirability traces.
The first six plots in the top row show traces of the predicted model. For each factor, the line in the plot shows how Rating varies when all other factors are set at the values defined by the red dashed vertical lines. By default, the profiler appears with categorical factors set at their low settings. By varying the settings for the factors, you can see how the predicted Rating for wines changes. Notice that a confidence interval is given for the mean predicted Rating.
Observe that Rater is not included among the factors shown in the profiler. This is because Rater is a block variable. You included Rater to explain variation, but Rater is not of direct interest in terms of optimizing process factor settings. The predicted Rating for a wine with the given settings is the average of the predicted ratings for that wine by all raters.
If you want to see the Profiler traces for the levels of Rater, perform the following steps:
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From the Prediction Profiler red triangle menu, select Reset Factor Grid.
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A Factor Settings window appears with columns for all of the factors, including Rater. The box under Rater and next to Show is not checked. This indicates that Rater is not shown in the Prediction Profiler.
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Click OK.
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Click in either plot above Rater.
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From the Prediction Profiler red triangle menu, select Maximize Desirability.
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To see predicted ratings for all runs, save the Prediction Formula. From the Response Rating red triangle menu, select Save Columns > Prediction Formula.
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A column called Pred Formula Rating is added to the data table. Note that one of the runs, row 33, was given the maximum rating of 20 by Rater 5. The predicted rating for that run by Rater 5 is 19.550. But the row 33 trial was run at the optimal settings. The predicted value of 19.925 given for these settings in the Prediction Profiler is obtained by averaging the predicted ratings for that run over all five raters.
When you maximized desirability, you learned that the optimal rating is achieved with the Dijon variety of grapes. See Prediction Profiler with Factor Settings Optimized. Your manager points out that it would be cost-prohibitive to replant the fields that are growing Bernard grapes with young Dijon vines. Therefore, you need to find optimal process settings and the predicted rating for Bernard grapes.
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Press Control and click in one of the Variety plots.
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From the Prediction Profiler red triangle menu, select Maximize Desirability.
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