DESIGN OF EXPERIMENTS WITH MINITAB EBOOK
Get this from a library! Design of experiments with MINITAB. [Paul G Mathews] -- Most of the classic DOE books were written before DOE software was generally. In this practical introduction to DOE, guided by the capabilities of the common software packages, Paul Mathews presents the basic types and methods of designed experiments appropriate for engineers, scientists, quality engineers, and Six Sigma Black Belts and Master Black Belts. Design of Experiments With MINITAB Hardcover – October 31, This item:Design of Experiments With MINITAB by Paul G. Mathews Hardcover $ A DOE Handbook:: A Simple Approach to Basic Statistical Design of.
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Despite its apparent simplicity, MINITAB also supports many advanced methods. • In addition to the tools required to design and analyze experiments, MINITAB. Cover design by Victoria Tomaselli. Cover illustration by Peter Hamlin. Minitab is a registered trademark of Minitab, Inc. SAS is a registered trademark of SAS. Similar Free eBooks. Filter by page count Statistical Design and Analysis of Experiments Design of experiments with MINITAB / Paul G. Mathews. p. cm.
The distinction between simple effects and main effects is subtle but important.
A simple effect of a factor is an effect at a particular combination of levels of the remaining factors. There are as many simple effects for each factor as there are combinations of levels of the remaining factors.
For example, the simple effect relevant to Question 1 is the conditional effect of changing breath from Off to On, assuming both prep and choose are set to Off. The simple effect relevant to Question 2 is the conditional effect of changing breath from Off to On, assuming both other factors are set to On.
Thus although Questions 1 and 2 both are concerned with simple effects of breath, they are concerned with different simple effects. A significant main effect for a factor is an effect on average across all combinations of levels of the other factors in the experiment.
For example, Question 3 is concerned with the main effect of breath, that is, the effect of breath averaged across all combinations of levels of prep and choose. Given a particular set of k factors, there is only one main effect corresponding to each factor.
Simple effects and main effects are not interchangeable, unless we assume that all interactions are negligible. Thus, neither necessarily tells anything about the other. A positive main effect does not imply that all of the simple effects are nonzero or even nonnegative. It is even possible due to a large interaction for one simple effect to be positive, another simple effect for the same factor to be negative, and the main averaged effect to be zero.
In the public speaking example, the answer to Question 2 does not imply anything about whether an intervention consisting of breath alone would be effective, or whether there would be an incremental effect of breath if it were added to an intervention initially consisting of choose alone.
Research questions, aliasing, and assumptions Suppose an investigator is interested in addressing Question 1 above. The answer to this research question depends only upon the particular simple effect of breath when both of the other factors are set to Off. The answer to Question 2, which also concerns a simple effect, depends only upon whether changing breath from Off to On has an effect on the outcome variable when prep and choose are set to On; it does not depend on establishing whether any other effects in the model are present or absent.
Thus, although there is aliasing in the individual experiments and comparative treatment strategies, these designs are appropriate for addressing Question 1, because the aliased effects correspond exactly to the effect of interest in Question 1. Similarly, although there is aliasing in the constructive treatment strategy, this design is appropriate for addressing Question 2.
In other words, although in our view it is important to be aware of aliasing whenever considering a reduced experimental design, the aliasing ultimately is of little consequence if the aliased effect as a package is of primary scientific interest.
The individual experiments and comparative treatment strategies would not be appropriate for addressing Question 2. The constructive treatment strategy could address Question 1, but only if breath was the first factor set high, with the others low, in the first non-control group. The conclusions drawn from these experiments would be limited to simple effects and cannot be extended to main effects or interactions. The situation is different if a reduced design is to be used to estimate main effects.
Suppose an investigator is interested in addressing Question 3, that is, is interested in the main effect of breath.
As was discussed above, in the individual experiments, comparative treatment, and constructive treatment approaches the main effect of breath is aliased with all the interactions involving breath.
It is appropriate to use these designs to draw conclusions about the main effect of breath only if it is reasonable to assume that all of the interactions involving breath up to the k-way interaction are negligible.
Then any effect of breath observed using an individual experiment or a single factor design is attributable to the main effect. The difference in the aliasing structure of fractional factorial designs as compared to individual experiments and single factor designs becomes particularly salient when the primary scientific questions that motivate an experiment require estimating main effects as opposed to simple effects, and when larger numbers of factors are involved.
However, the small three-factor fractional factorial experiment in Table 2 can be used to demonstrate the logic behind the choice of a particular fractional factorial design. If it is reasonable to assume that this two-way interaction is negligible, then it is appropriate to use this fractional factorial design to estimate the main effect of breath. In general, investigators considering using a fractional factorial design seek a design in which main effects and scientifically important interactions are aliased only with effects that can be assumed to be negligible.
Many fractional factorial designs in which there are four or more factors require many fewer and much weaker assumptions for estimation of main effects than those required by the small hypothetical example used here.
For these larger problems it is possible to identify a fractional factorial design that uses fewer experimental conditions than the complete design but in which main effects and also two-way interaction are aliased only with interactions involving three or more factors.
Many of these designs also enable identification of some three-way interactions that are to be aliased only with interactions involving four or more factors. The Seven Management and Planning Tools. Applied Benchmarking for Competitiveness: Richard Keegan. Joseph P.
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Experimental Design and Process Optimization with Minitab
Verifique seu carrinho. Continue comprando Check-out Continue comprando. Chi ama i libri sceglie Kobo e inMondadori. Solid models of the femur and tibia and geometries of soft tissues, including articular cartilages and menisci, were developed from the magnetic resonance images MRI.
Optimize Attribute Responses Using Design of Experiments
Each image was taken at 3. The model consisted of two bony structures femur and tibia , both the femoral and tibial articular cartilages, and both the medial and lateral menisci. Figure 1 shows the generated 3D model in details. The model did not include ligaments. The finite element mesh generation was performed leading to linear 4-noded tetrahedron elements for articular cartilage and menisci for femoral cartilage, for tibial cartilage, for medial meniscus, and for lateral meniscus.
Contact was defined between the femoral cartilage and meniscus, the meniscus and tibial cartilage, and femoral cartilage and tibial cartilage for both lateral and medial compartments, resulting in six contact-surface pairs. Completely general contact condition involving small sliding of pairs was applied on the model and all contact surfaces were modeled as frictionless.
The cartilage in the knee is a complex structure, composed mainly of networks of collagen fibrils that embed water and a non-fibrillar matrix. The cartilage is known to be inhomogeneous and anisotropic material, but considering that the loading time of interest is related to a single leg stance and that the viscoelastic time constant for cartilage is approximately seconds from biphasic theory [ 3 , 9 ], the elastic solution does not diverge from the biphasic solution [ 17 ].
The cartilage, therefore, was assumed to behave as a homogeneous linearly isotropic elastic material for contact pressure computations, similar to the previous studies [ 18 , 19 ]. The meniscus, also, has similar structure to that of cartilage and it is also known to be inhomogeneous and anisotropic material, but various material property definitions can be found in the literature for this component [ 20 — 22 ].
Furthermore, the meniscus has a time constant, as large as seconds [ 9 ], and can also be considered as an elastic material for compression of the joint during the short loading times single leg stance.
In this study, the menisci were treated as linearly elastic, transversely isotropic material to represent the circumferential fiber arrangement. Femur and tibia were represented by rigid bodies because this is time efficient in a nonlinear analysis and accurate due to their much larger stiffness compared to that of soft tissues.You'll learn about: Accessing powerful Minitab functions with the Minitab assistant Confidence, prediction, and tolerance intervals Designing and analyzing experiments with hard-to-change variables Statistical process control SPC , Six Sigma applications, and quality control Predicting the economic impact of sampling Analyzing life data with additional variables Simple enough for a beginner, challenging enough for an advanced student, and thorough enough for a Six Sigma professional, Minitab Demystified is your shortcut to statistical analysis success!
Yet there are few areas in the social and behavioral sciences in which theory makes specific predictions about higher-order interactions, and it appears that to date there has been relatively little empirical investigation of such interactions. One guiding principle is called Hierarchical Ordering. Interpret the Results -- 4. Little Green Apples Publishing. The cumulative effect of adding breath would be assessed by comparing experimental conditions 5 and 7.
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