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| bio | website | |
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| location | ||
| age | ||
| visits | member for | 2 years, 2 months |
| seen | Jan 11 at 20:45 | |
| stats | profile views | 15 |
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Oct 31 |
awarded | Scholar |
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Oct 31 |
awarded | Supporter |
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Oct 31 |
accepted | Post hoc test of adjusted means (after ANCOVA) |
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Sep 27 |
comment |
Some questions about statistical randomness Sorry if I misstated your (@whuber) position. You stated, "...in any 100 flips of a fair coin, we would expect to observe six heads or six tails in a row--and that is a 'truly nonrandom pattern'..." I intrepreted it that you were saying a random process will generate nonrandom patterns. |
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Sep 26 |
awarded | Commentator |
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Sep 26 |
comment |
Some questions about statistical randomness I see no contradiction. You seem to think that random generators create nonrandom patterns. That is the contradiction. You are arguing that truly random processes will generate non random observations. What you are describing is sometomes called the "clustering illusion", which is the tendency to incorrectly perceive clusters from random distributions. All I am saying is that if a process creates nonrandom observations, then it isn't random. You argue that you expect a random process to create strings of nonrandom observations, yet you call that nonrandom. Classic example of Apophenia. |
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Sep 26 |
comment |
Some questions about statistical randomness Really? I would have thought that, since one expects to see strings of heads of tails from a random number generator, that when we do see it, we should not be surprised. Why consider it to be nonrandom? If one had a number generator that did 100 flips, and it purposefully avoided 4 or more heads or tails in a row, it would look more random than a truly random process, but it would actually be nonrandom. A naive view of randomness is the lack of all patterns - but that would be nonrandom. |
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Sep 26 |
answered | Some questions about statistical randomness |
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Sep 26 |
awarded | Teacher |
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Sep 26 |
answered | Detecting outliers using standard deviations |
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Sep 25 |
comment |
Post hoc test of adjusted means (after ANCOVA) @Damian, I will look into those post hoc tests you suggest, thanks. But I guess I am wondering why the use of post hoc tests on adjusted means is not more common. Semi-rhetorical question: Do most people (non statisticians, but users of statistics) know that ANCOVA post hoc tests are often on original means? |
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Sep 25 |
comment |
Post hoc test of adjusted means (after ANCOVA) @Michael Chernick, I was hoping for a more general consideration. Say I was interested in comparing concentrations of a certain protein in animals on different diets, but there was a range of ages among the animals, age also varied among treatments, and protein concentration was proportional to age. If I want to know the differences in protein concentrations between diets, I could have age as a covariate. If I wanted to make claims whether diet A caused lower protein than diet B and C, etc, independent of age, would I not want to use post hoc tests on adjusted means? |
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Sep 25 |
comment |
Post hoc test of adjusted means (after ANCOVA) @Henrik, According to the Statistica website, "STATISTICA will always compute post-hoc tests using observed means, taking the estimate of Sigma (when appropriate, i.e., when called for by the respective test) from the overall analysis (ANOVA). For ANCOVA designs, even though all post-hoc tests are performed on observed means, the estimate of Sigma for the tests will be "adjusted" by the presence of covariates in the model (because the MS-error for the between-group design will have been effectively adjusted)." |
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Sep 24 |
asked | Post hoc test of adjusted means (after ANCOVA) |
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Mar 24 |
awarded | Student |
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Mar 24 |
comment |
Repeated measures with correlated measures (not time) Yes, I remember that paper from sci.stat.math, posted by the author himself... |
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Mar 24 |
comment |
Repeated measures with correlated measures (not time) Appreciate the help. I have already been using multivariate stats on the data – discriminant function analysis. But out of curiosity I had decided to run a repeated measures. Shall I assume that it is the merely the least preferable option compared to your better alternatives, or is it an invalid option entirely? |
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Mar 24 |
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Repeated measures with correlated measures (not time) Thanks! As a further aside, I had thought that was one of the advantages of Partial Least Squares (compared with doing regression following PCA) – the first component automatically has the highest correlation with Y, the 2nd component the second highest correlation, etc... |
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Mar 24 |
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Repeated measures with correlated measures (not time) Yes, PCA or Factor Analysis is frequently used with this type of data. But those answer different questions, do they not? I had thought that someone would suggest MANOVA, though the idea of having 30 variables is daunting... I was hoping that repeated measures would be valid, as it is easier to interpret, allows one to compared individual compounds between treatments (post hoc), and it can (?) handle nonindependent variables. I will looking into using MANOVA |
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Mar 24 |
comment |
Repeated measures with correlated measures (not time) Hi, I don't normally see folks use repeated measures in such a fashion. My question is to see if one can. All the compounds are in the same units (eg ng/g). One would not routinely compare compound X with compound Y (though one occasionally does). Following the Diet X FA interaction, a posthoc test could compare compound X between diets, compound Y between diets, etc. My main impetus in asking this question is that none of the compounds are independent of each other. Thus, if compound X varies among diets, then chances are other compounds do as well. What is a better way than 30 ANOVAs? |
