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1. ### ANOVA analysis of two site management types

I read it as whether either conventional or special has more protein which is two sided.
2. ### ANOVA analysis of two site management types

Two sided perhaps?

I think it means this - The normal scores are humped around 21 and scores on each side are less likely than scores near 21. There will be a score on the low side of 21 which is as likely as 32 on the high side. Normal is symmetric so it shouldn't be too hard to work out what that low score is.
4. ### Can you Generating Meaningful Confidence Intervals from logarithmic data?

You need to keep in mind that a confidence interval is the range of plausible values that the true value could lie in. The method you are trying is not suitable in this case. For a start, the values you get aren't independent. If the first sample gives 9000, then the second sample is likely to...
5. ### Can you Generating Meaningful Confidence Intervals from logarithmic data?

I think that they mean 90% consistency as you explained it before. If you give a true 9000 to 10 labs, then 9/10 labs will report 9000. However it seems to be a little dishonest of the manufacturer to claim that this means 90% accuracy. Also, if you take a graded series of concentrations from...
6. ### Can you Generating Meaningful Confidence Intervals from logarithmic data?

Thanks for that. I'm just guessing here from your comments and calculations. Is this the sort of thing? You take your sample, do something to it, and see how long it takes to react. If it takes 1 day record 500000, 3 days record 35000, 4 days record 9000. and probably something like 2 days...
7. ### Can you Generating Meaningful Confidence Intervals from logarithmic data?

In general, if you have somehow managed to find the lower and upper limits for a CI on logged data, you can just "unlog" these limits more or less as you have described to get an asymmetric CI on the raw data. However, I suspect that things are more complicated here. If you got 9000, 9000, 9000...
8. ### Exploring non linear relationship between two variables

You are interested in how metamemory scores (DV) depend on objective memory scores (IV). Perhaps you should draw the graph with the objective score (IV) on the x axis. The regression lines now have a quite different look.
9. ### Exploring non linear relationship between two variables

What are the two axes?
10. ### Exploring non linear relationship between two variables

Quartiles do have an arbitrary feel about them that tends to make statisticians feel a bit uncomfortable. Could you perhaps post a graph of metamemory against measured memory, or perceived memory against memory (or both). kat
11. ### Exploring non linear relationship between two variables

Do you have something you want to prove? Do you need a p value for a paper or report? The graph of metamemory vs memory sounds U shaped. One common way to analyse this is to include a quadratic term.
12. ### what type of analyses to use if one of the dependent variables is omitted when another dependent variable takes a certain value?

You could try two separate analyses, the second using a reduced set of IV. Are M or F more likely to visit NY? Of those who visit, do M or F like it more?
13. ### Fisher r-to-z transformation: comparison of non-significant rs

OK. Sample size certainly can reduce the power to distinguish between the two correlations, but I still think that it is valid to test. Are you hoping that they are different, or the same? In any event, if you're not comfortably with testing, don't. Report "no sig diff" bearing in mind that this...

15. ### Fisher r-to-z transformation: comparison of non-significant rs

I think the statistics works. It is certainly possible that a true weak positive correlation and a true weak negative correlation can be shown to be different, while at the same neither correlation can be shown different from 0 from the data. However, the difference may be too small to be of...
16. ### Exponential fit slopes comparisson

I think so, unless your y=a*b^x analysis gave you SE and df for b. If it did, there is an easy conservative approach you can use.
17. ### Exponential fit slopes comparisson

Which do you regard as the slope, a or b? Do you have data for both? Have you tried using y=a.exp(mx) or ln(y) = A +mx and using the linear model test?
18. ### Constant sum question - results analysis

With one test there is no problem. If your final analysis is bristling with p values, some of which are hovering just below 0.05, then some caution is needed. Bonferroni is conservative, but the fancy alternatives aren't much better. You can always explain the problem and report p = 0.04 as...
19. ### Beta - Cov(x,y)/Var(x) or SC(x,y)/SS(x)

Start with Σ (Xi - Xbar) (Yi-Ybar)/Σ (Xi - Xbar)^2 written as a fraction Divide top and bottom by n, It turns into Cov(x,y)/Var(x)
20. ### Constant sum question - results analysis

OK. Thanks issta. In my opinion, there is nothing wrong with the paired t test for this. With 600 subjects it will give you p values that can be relied on, even if the data isn't perfectly normal. (Other forum members may disagree. We'll see.) My view is to keep the analysis simple - the paired...