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# Split-Plot General Factorial Tutorial Introduction - Statease.info

Split-Plot General Factorial Tutorial Introduction - Statease.info

## Effects graph –

Effects graph – interaction of temperature by pulp preparation on Day 1By default, the graph comes up for Day 1 (lowest level of factor A). On the FactorsTool press the second button on Replicate to see the predicted results for Day 2.Setting Replicate to Day 2Notice how the graph shifts very slightly. Click on any point to get the identification.The round symbols represent actual values, which are displayed to the left of the plotwhen you click the point. The other symbols (triangle, square and diamond in varyingcolors) show the predicted means. Click any of these points to get the value for tensilestrength.Now press the third button to see the predicted results for Day 3. However, since timecannot be controlled, it might be best to show the BC interaction at an average of theDays. As shown below, click on the list arrow by the Day field and select Average.12 • Split-Plot General Factorial Tutorial Design-Expert 7 User’s Guide

DX7-02E-SplitPlotGen.doc Rev. 4/10/06Setting replicates to average levelFrom this graph we can see pulp preparation B2 will maximize tensile strength.Evidently it will be most robust to temperature changes, especially in the temperaturerange of 225 to 275 degrees F. This relationship holds true regardless of which day(replicate) you select (the buttons on the tool palette).To preserve the modeling used to produce the plots, select File, Save or click the saveicon .This is the end of the story, but as a postscript, you may wonder if it was worth all thebother to properly account for the split-plot nature of this experiment. Click back to theANOVA for this “All effects” analysis and then back to the ones for “Whole”, “Subplot” and “Interaction.” The differences are relatively subtle. In other words, in thisparticular case, an experimenter who unwittingly ignored the restrictions inrandomization caused by the split plot may have arrived at the same general conclusionsshown here. In any case, it is always wise to assess the practical importance of theobserved effects and, if warranted, follow up by doing confirmation runs to ensure thatpredictions from the model will be relatively accurate.Design-Expert 7 User’s Guide Split-Plot General Factorial Tutorial • 13

Introduction to the Factorial Designs
Introduction to Factorial ANOVA Designs
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