Reverse of Fisher's r to z
Hi everyone, I have an itchy little question about the familiar Fisher's r to z transformation: The formula, expressed as z= sqrt (log e ( (1+r)/(1-r))), is in pretty much any older stats textbook. Does anyone know of a source where the equation is written to solve for r? I know it's a very uncommon use (if used at all in this way ), but I've got a very legitimate research need (and my brain's doing odd things when I'm trying to rewrite the equation). Thanks in advance, Cherilyn = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Funding for Posters: European Nutrition and Cancer Conference
Dear All, The European Conference on Nutrition and Cancer will take place in Lyon, France on 21-24 June 2001. An important feature of the conference is 2 large poster sessions on days 2 and 3. As indicated on the programme web site, in the GENERAL INFORMATION section, funds have been set aside to pay for travel expenses and lodging for up to 50 participants presenting posters. Poster abstracts must be submitted by the 30th April, 2001. Posters concerning studies of diet, nutrition, genetics, hormones, epidemiologic and statistical methods or other related areas of research are welcome. The form for submitting abstracts is available on the Conference web site: http://www.nutrition-cancer2001.com For further information send email to: [EMAIL PROTECTED] = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
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Re: rotations and PCA
Eric Bohlman wrote: In science, it's not enough to say that you have data that's consistent with your hypothesis; you also need to show a) that you don't have data that's inconsistent with your hypothesis and b) that your data is *not* consistent with competing hypotheses. And there's absolutely nothing controversial about that last sentence [...] Well, I'd want to modify it a little. On the one hand, a certain amount of inconsistency can be (and sometimes must be) dealt with by saying "every so often something unexpected happens"; otherwise it would only take two researchers making inconsistent observations to bring the whole structure of science crashing down. And on the other hand there are _always_ competing hypotheses. [Consider Jaynes' example of the policeman seeing one who appears to be a masked burglar exiting from the broken window of a jewellery store with a bag of jewellery; he (the policeman) does *not* draw the perfectly logical conclusion that this might be the owner, returning from a costume party, and, having noticed that the window was broken, collecting his stock for safekeeping.] It is sufficient to show that your data are not consistent with hypotheses that are simpler or more plausible, or at least not much less simple or plausible. -Robert Dawson = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Time Series Data. Significant movement
I have a set of measurements (e.g. number of errors, faults, etc) over a period of time (e.g. 9 Months) and measurements are taken weekly. These measurements are graphed on a spreadsheet. I need to select a small number of measurements and graphs then display the measurements and the graphs to my audience. My question is, Is there a statistical way of selecting the set of measurement that show movement up or down other than just eye balling the graphs?? -- Philip [EMAIL PROTECTED] = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: Reverse of Fisher's r to z
Thanks-- my algebra (and apparently my eyesight too) has gotten a bit creepy around the edges, so I didn't trust it for something this important Truly appreciate it!!! Best, Cherilyn On Mon, 9 Apr 2001, Will Hopkins wrote: It's elementary algebra, Cherilyn. BTW, it's z = 0.5log..., not sqrt. So r = (e^2z - 1)/(e^2z + 1). Will = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: Reverse of Fisher's r to z
Yes, there are reasons for using the transformation frm z to r. And, there are published tables of this. For example, Appendix Table B.19 of Zar, Biostatistical Analysis, 4th ed., 1999. Jerrold H. Zar, Professor Department of Biological Sciences Northern Illinois University DeKalb, IL 60115 [EMAIL PROTECTED] === Will Hopkins [EMAIL PROTECTED] 04/09/01 04:29AM It's elementary algebra, Cherilyn. BTW, it's z = 0.5log..., not sqrt. So r = (e^2z - 1)/(e^2z + 1). Will = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: In realtion to t-tests
On Mon, 09 Apr 2001 10:44:40 -0400, Paige Miller
[EMAIL PROTECTED] wrote:
"Andrew L." wrote:
I am trying to learn what a t-test will actually tell me, in simple terms.
Dennis Roberts and Paige Miller, have helped alot, but i still dont quite
understand the significance.
Andy L
A t-test compares a mean to a specific value...or two means to each
other...
[ ... ]
I remember my estimation classes, where the comparison was
always to ZERO for means. To ONE, I guess, for ratios.
Technically speaking, or writing.
For instance, if the difference in averages X1, X2 is expected to
be zero, then "{(X1-X2) -0 }" ... is distributed as t . It might
look like a lot of equations with the 'minus zero' seemingly tacked
on, but I consider this to be good form. It formalizes as
term minus Expectation of term
--
Rich Ulrich, [EMAIL PROTECTED]
http://www.pitt.edu/~wpilib/index.html
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Re: normal approx. to binomial
James Ankeny [EMAIL PROTECTED] wrote: : My question is, are they saying that the sampling : distribution of a binomial rv is approximately normal for large n? : It's a special case of the CLT for a binary variable with probability p, taking the sum of n observations = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: normal approx. to binomial
one tech issue, one thinking issue, I believe. 1) Tech: if np _and_ n(1-p) are 5, the distribution of binomial observations is considered 'close enough' to Normal. So 'large n' is OK, but fails when p, the p(event), gets very small. Most examples you see in the books use p = .1 or .25 or so. Modern industrial situations usually have p(flaw) around 0.01 and less. Good production will run under 0.001. To reach the 'Normal approximation' level with p = 0.001, you have to have n = 5000. Not particularly reasonable, in most cases. If you generate the distribution for the situation with np = 5 and n = 20 or more, you will see that it is still rather 'pushed' (tech term) up against the left side - your eye will balk at calling it normal. But that's the 'rule of thumb.' I have worked with cases, pushing it down to np = 4, and even 3. However, I wouldn't want to put 3 decimal precision on the calculations at that point. My personal suggestion is that if you believe you have a binomial distribution, and you need the confidence intervals or other applications of the distribution, then why not simply compute them out with the binary equations. Unless n is quite large, you will have to adjust the limits to suit the potential observations, anyway. For example, if n = 10, there is no sense in computing a 3 sigma limit of np = 3.678 - you will never measure more precisely than 3, and then 4. But that's the application level speaking here. 2)I think your books are saying that, when n is very large (or I would say, when np5), the binomial measurement will fit a Normal dist. It will be discrete, of course, so it will look like a histogram not a continuous density curve. But you knew that. I think your book is calling the binomial rv a single measurement, and it is the collection of repeated measurements that forms the distribution, no? I explain a binomial measurement as, n pieces touched/inspected, x contain the 'flaw' in question, so p = x/n. p is now a single measurement in subsequent calculations. to get a distribution of 100 proportion values, I would have to 'touch' 100*n. I guess that's OK, if you are paying the inspector. Clearly, one of the draw backs of a dichotomous measurement (either OK or not-OK) is that we have to measure a heck of a lot of them to start getting decent results. the better the product (fewer flaws) the worse it gets. See the situation for p = 0.001 above. Eventually we don't bother inspecting, or automate and do 100% inspection. So the next paragraph better explain about the improved information with a continuous measure... Sorry, I got up on my soap box by mistake. Is this enough explanation? Jay James Ankeny wrote: Hello, I have a question regarding the so-called normal approx. to the binomial distribution. According to most textbooks I have looked at (these are undergraduate stats books), there is some talk of how a binomial random variable is approximately normal for large n, and may be approximated by the normal distribution. My question is, are they saying that the sampling distribution of a binomial rv is approximately normal for large n? Typically, a binomial rv is not thought of as a statistic, at least in these books, but this is the only way that the approximation makes sense to me. Perhaps, the sampling distribution of a binomial rv may be normal, kind of like the sampling distribution of x-bar may be normal? This way, one could calculate a statistic from a sample, like the number of successes, and form a confidence interval. Please tell me if this is way off, but when they say that a binomial rv may be normal for large n, it seems like this would only be true if they were talking about a sampling distribution where repeated samples are selected and the number of successes calculated. ___ Send a cool gift with your E-Card http://www.bluemountain.com/giftcenter/ = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ = -- Jay Warner Principal Scientist Warner Consulting, Inc. North Green Bay Road Racine, WI 53404-1216 USA Ph: (262) 634-9100 FAX:(262) 681-1133 email: [EMAIL PROTECTED] web:http://www.a2q.com The A2Q Method (tm) -- What do you want to improve today? = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
US government grants and scholarships for International students.
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Re: Reverse of Fisher's r to z
Cherilyn Young wrote:
I have an itchy little question about the familiar Fisher's r to z
transformation: The formula, expressed as z= sqrt (log e ( (1+r)/(1-r))),
is in pretty much any older stats textbook. Does anyone know of a source
where the equation is written to solve for r? I know it's a very uncommon
use (if used at all in this way ), but I've got a very legitimate research
need (and my brain's doing odd things when I'm trying to rewrite the
equation).
r.back - function(x)
{
((2.71828182845905^(2 * x)) - 1)/((2.71828182845905^(2 * x)) + 1)
}
fish.z - function(x)
{
ifelse(x == 0, 0, 0.5 * log((1 + abs(x))/(1 - abs(x))) * (x/abs(x)))
}
Examples:
fish.z(.45)
[1] 0.4847003
r.back(.4847003)
[1] 0.45
r.back(fish.z(.45))
[1] 0.45
HTH,
Chuck
-
Chuck Cleland
Institute for the Study of Child Development
UMDNJ--Robert Wood Johnson Medical School
97 Paterson Street
New Brunswick, NJ 08903
phone: (732) 235-7699
fax: (732) 235-6189
http://www2.umdnj.edu/iscdweb/
http://members.nbci.com/cmcleland/
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