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I have studied at least a little combinatorics, and the language here is completely inpenetrable to me. Neither the question of "what property is this design su

by noqc 3y ago

I have studied at least a little combinatorics, and the language here is completely inpenetrable to me. Neither the question of "what property is this design supposed to have?" or "why does it have it?" are stated in plain or even mathematical english. The wiki entry is similarly unhelpful.

acyou 3y ago
It's Design of Experiments language, DOE being a damned useful tiny little subset of applied statistics.
usgroup 3y ago
In a full factorial design you’re able to calculate the main effects and all the interactions between everything and everything else: not just pairwise but all combinations of variables. You may not care for that because you don’t think there are such interactions or perhaps you just don’t have the experimental budget. In fractional designs you can chose to confound some of the interactions so that you can’t tell what’s happening beyond a certain level. Eg just main and pairwise effects, with the rest confounding those effects. You would do this because it requires fewer runs. You can also use it as a sort of screener when you have many variables by confounding things together in such a way that you can at least tell which variables are having the effects even if you can’t calculate the individual effects.
crispyambulance 3y ago
For whatever reasons, much of the community that does DOE's is beset with heavy use of opaque jargon and flagrant use of "canned tests" from software packages like minitab. Making statistical inferences from data is nuanced and complicated if the thing you're interested in doesn't just pop-out from simple descriptive statistics treatments (histograms, box-plots, scatterplots, simple linear models). There are some islands of sanity: - NIST has a great resource (https://www.itl.nist.gov/div898/handbook/index.htm https://www.itl.nist.gov/div898/handbook/index.htm). Don't be alarmed by the retro html appearance. It's actually meticulously maintained and contains reproduceable examples with data. The plain language is such a relief compared to other references. - "Statistics for Experimenters" the book by George Box et al, is a great resource that's fairly comprehensive in my opinion. Good, clear writing. It seems to be widely available online (don't know if that's intentional).

by noqc 3y ago

acyou 3y ago

It's Design of Experiments language, DOE being a damned useful tiny little subset of applied statistics.

usgroup 3y ago

In a full factorial design you’re able to calculate the main effects and all the interactions between everything and everything else: not just pairwise but all combinations of variables. You may not care for that because you don’t think there are such interactions or perhaps you just don’t have the experimental budget. In fractional designs you can chose to confound some of the interactions so that you can’t tell what’s happening beyond a certain level. Eg just main and pairwise effects, with the rest confounding those effects. You would do this because it requires fewer runs. You can also use it as a sort of screener when you have many variables by confounding things together in such a way that you can at least tell which variables are having the effects even if you can’t calculate the individual effects.

crispyambulance 3y ago

For whatever reasons, much of the community that does DOE's is beset with heavy use of opaque jargon and flagrant use of "canned tests" from software packages like minitab. Making statistical inferences from data is nuanced and complicated if the thing you're interested in doesn't just pop-out from simple descriptive statistics treatments (histograms, box-plots, scatterplots, simple linear models). There are some islands of sanity: - NIST has a great resource (https://www.itl.nist.gov/div898/handbook/index.htm https://www.itl.nist.gov/div898/handbook/index.htm). Don't be alarmed by the retro html appearance. It's actually meticulously maintained and contains reproduceable examples with data. The plain language is such a relief compared to other references. - "Statistics for Experimenters" the book by George Box et al, is a great resource that's fairly comprehensive in my opinion. Good, clear writing. It seems to be widely available online (don't know if that's intentional).