By Andrew Sleeper
In today’s aggressive atmosphere, businesses can now not produce items and companies which are in basic terms stable with low illness degrees, they need to be near-perfect. layout for 6 Sigma facts is a rigorous mathematical roadmap to assist businesses succeed in this target. because the 6th booklet within the Six Sigma operations sequence, this finished e-book is going past an advent to the statistical instruments and techniques present in such a lot books yet includes professional case experiences, equations and step-by-step MINTAB guide for acting: DFSS layout of Experiments, Measuring approach potential, Statistical Tolerancing in DFSS and DFSS ideas in the offer Chain for enhanced effects. the purpose is that can assist you greater analysis and root out power difficulties sooner than your services or products is even introduced.
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Extra info for Design for Six Sigma statistics : 59 tools for diagnosing and solving problems in DFSS initiatives
Example text
The horizontal scale of the dot graph represents the tolerance range for volume. Bill notices that all four were within tolerance, but all were low. Also, there was quite a bit of variation between these four units. Bill’s team designs an experiment to determine how the volume reacts to changes in three components that they believe to be critical. The team’s objective is to develop a model for volume as a function of these three components, and then to use that model to optimize the system. Table 1-4 lists the three factors and the two levels chosen by the team for this experiment.
We may conclude that X causes Y, or Y causes X, or an unseen third variable causes the behavior in both X and Y, or none of the above. Most graphs and most statistical analysis indicate only where relationships exist. We must add our knowledge to inference before we can reach a conclusion about cause and effect. To illustrate this process, look at Figure 2-2, which shows the mileage and engine size of all two-seater cars listed in the 2004 DOE/EPA Fuel Economy Scientific theory Y X and Y are related Y = f(X) X Inferred relationship Figure 2-1 Experience Cognitive Process Triggered by Viewing a Graph Conclusion about cause and effect Visualizing Data 33 Miles per gallon - city 60 50 40 30 20 10 0 1 2 3 4 5 6 7 8 9 Engine size (L) Scatter Plot of City Fuel Economy and Engine Size for Two-Seater Cars Listed in The 2004 Fuel Economy Guide (United States, 2004).
It is good practice to recognize when we are inferring a relationship, and to separate this process from drawing conclusions about cause and effect. For a Black Belt or statistician, this distinction is particularly important. When acting as a consultant, a critical role of a Black Belt or statistician is to apply appropriate methods and infer relationships from data. To reach conclusions about cause and effect requires participation from process owners, engineers, technicians, and operators who understand the underlying process and the science behind it.