17:45 Sep 28, 2016 |
Spanish to English translations [PRO] Tech/Engineering - Mathematics & Statistics / Statistical analysis | |||||||
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| Selected response from: neilmac Spain Local time: 08:11 | ||||||
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Summary of answers provided | ||||
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4 +1 | combination |
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4 | two-factor interaction term |
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4 -1 | crossover |
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Discussion entries: 1 | |
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two-factor interaction term Explanation: This seems like a complete 2^5 factorial design. |
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crossover Explanation: Used to describe interactions: http://www.theanalysisfactor.com/interactions-main-effects-n... -------------------------------------------------- Note added at 48 mins (2016-09-28 18:34:31 GMT) -------------------------------------------------- NB: DLyons is the expert in this area (soy de letras)... Reference: http://www.sciencedirect.com/science/article/pii/S0378381298... https://es.wikipedia.org/wiki/Crossover |
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combination Explanation: https://en.wikipedia.org/wiki/Combination GLM = General Linear Model cruce = the combination of more than two variables to run a GLM regression -------------------------------------------------- Note added at 13 hrs (2016-09-29 06:57:08 GMT) -------------------------------------------------- https://en.wikipedia.org/wiki/General_linear_model -------------------------------------------------- Note added at 1 day13 hrs (2016-09-30 07:27:40 GMT) -------------------------------------------------- Erratum:'COMBINATION OF TWO DIFFERENT VARIABLES TO RUN A GLM REGRESSION' instead of 'the combination of more than two variables to run a GLM regression' -------------------------------------------------- Note added at 1 day13 hrs (2016-09-30 07:39:59 GMT) -------------------------------------------------- To compute the number of combinations, on can build a matrix with 10 columns and rows. Each non-diagonal element of the matrix is a combination of two different variables. So the number of combinations is equal to 10X10 -10 = 100-10 = 90. 100 is the number of combinations (cruce) and there are 10 diagonal combinations of IDENTICAL variables |
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