Six concepts underpin the entire menu. Without them, the screens and experimental designs are difficult to understand.<\/p>\n\n\n\n
A factor<\/strong> (labeled X) is a variable that you control: a temperature, a pressure, a machine, or a supplier. Its levels<\/strong> are the values we will set it to: two levels (min\/max), three (min\/middle\/max), or more. A essay<\/strong> is a line on the plan: a combination of levels to be achieved.<\/p>\n\n\n\n The table of factors<\/strong>, on the left side of each page, is the source of truth: you enter one factor per line, along with its levels. The number of factors and their levels determine which designs are suggested; they are not entered anywhere else.<\/p>\n\n\n\n There are interaction<\/strong> between two factors when the effect of one depends on the level of the other: the hardener improves cold strength but reduces it at high temperatures. An interaction cannot be inferred from any factor-by-factor study; this is precisely what the design of experiments provides.<\/p>\n\n\n\n \u26a0\ufe0f Every interaction you want to estimate uses one degree of freedom<\/strong>, as an additional factor. That\u2019s why declaring interactions causes the small tables to disappear from the list: there\u2019s no longer room for them.<\/p>\n\n\n\n A fractional table only covers a portion of the possible combinations. The trade-off: some effects become aliases<\/strong>, that is, when combined, their columns are identical, and nothing in the data will indicate which of the two caused the observed discrepancy.<\/p>\n\n\n\n Visit resolution<\/strong> summarizes the seriousness of these misunderstandings:<\/p>\n\n\n\n \ud83d\udca1 The alias table<\/strong>, at the bottom of the Taguchi pages, lists\u2014column by column\u2014the factors and their interactions. This is the document you should read before<\/strong> to begin the trials: he states in advance what the plan will not be able to resolve.<\/p>\n\n\n\n Two columns in the table are orthogonal<\/strong> when their correlation is zero: their effects are estimated independently. This is the design objective for all the plans in the menu, and what is verified by the correlation matrix<\/strong> Available on multiple pages. Outside the diagonal, we want white.<\/p>\n\n\n\n A key point<\/strong> is a midpoint test of all factors. It is not used to estimate an effect, but rather for two other purposes: to measure the repeatability<\/strong> of the process, and detect a curvature<\/strong>, if the observed value deviates from the average of the extremes, the response is not linear.<\/p>\n\n\n\n Reply<\/strong> One approach doubles the number of trials and removes aliases. This is the simplest\u2014and most expensive\u2014solution.<\/p>\n\n\n\n The number of degrees of freedom<\/strong> (ddl) is the number of coefficients to be estimated: the constant, the main effects, the declared interactions, and the quadratic terms. A design requires at least as many trials as there are ddl, plus one additional trial per ddl, if residual variance is also to be estimated.<\/p>\n\n\n\n \u26a0\ufe0f This is a hard constraint of the design of experiments, and it is non-negotiable: requesting a rich model with a tight trial budget is impossible\u2014not just imprecise. The menu pages use this constraint to hide tables that are too small, rather than letting you build an unusable design.<\/p>","protected":false},"featured_media":0,"menu_order":20,"template":"","meta":{"_acf_changed":false},"menu-guide-dutilisateur":[24],"class_list":["post-4648","guide-dutilisateur","type-guide-dutilisateur","status-publish","hentry","menu-guide-dutilisateur-7-doe-plans-dexperiences"],"acf":[],"yoast_head":"\n2. Interaction<\/h2>\n\n\n\n
3. Aliases and Resolution<\/h2>\n\n\n\n
Resolution<\/strong><\/td> What Is Being Confused<\/strong><\/td> Reading<\/strong><\/td><\/tr><\/thead> III<\/strong><\/td> Major effects with interactions<\/td> Screening only. An effect may be a disguised interaction.<\/td><\/tr> IV<\/strong><\/td> Interactions among them; specific main effects<\/td> A good compromise: the results are reliable.<\/td><\/tr> V and up<\/strong><\/td> Almost nothing<\/td> Comfortable modeling.<\/td><\/tr> Complete factorial<\/strong><\/td> Nothing<\/td> All combinations have been completed.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n 4. Orthogonality and Correlation<\/h2>\n\n\n\n
5. Central Points and Replication<\/h2>\n\n\n\n
6. Degrees of Freedom<\/h2>\n\n\n\n