What is the error % in ELISA test?
139983
This depends on the test: What antigen are you testing for, how was it produced etc. Different ELISAs can have different sensitivity and specitivity.\nFor a better answer you have to give more details in your question.
657317
It depends on the ELISA method you are trying to use and the antigen of interest.  In the most basic sense if the cow Ig also recognized the antigen in question, then it would be possible to use a human-designed assay with cow antibody.  There is also no reason a bovine-specific ELISA assay couldn't be developed.
117941
Go to the Quest Diagnostics site, and type in Bun.\nhttp://www.questdiagnostics.com/kbase/topic/medtest/hw4322/results.htm
585189
can you be more specific as to what the error is or what it says exactly ?\n\nFrom what you have said all I can say is that it is self explanatory ie its scripts that have errors, which doesnt really help much.
753347
Use the following - this will work:\n\nIf IsError(Range("A1")) Then\n... Code\nEnd If
349744
I'm assuming you're referring to Type I and Type II error, which has to do with hypothesis testing.  In hypothesis testing, we're interested in whether or not a given scenario is consistent or not with a null hypothesis.  For example, say that past data states that the average income for an american household is $35k a year (this is the null hypothesis).  We think that number has gone up significantly since the last study.  So, we collect a bunch of data, to see if we're right.  In order to compare the two, you need to calculate a test statistic, and in order to limit your chance of making a mistake, you select an alpha level (which is usually 0.05 in most statistics textbooks).  This alpha level is your probability of making a type I error, which is when you reject the null hypothesis when it's actually true.  Meaning, your data told you that the mean income had gone up significantly, but by random chance you just happened to sample higher than average income families, and it actually hadn't gone up at all.  The other type of error is Type II error, which is when you fail to reject the null hypothesis when it's actually false.  In terms of the example:  We happened to collect data that said that the mean income hadn't changed, but it actually has.  This error is related to a statistic called 'power' - the higher the power of your test, the less likely you'll make a type II error.  However, it's generally impossible to increase the power of your test without also increasing the alpha, or the probability you'll make a type I error, and most of the time, making a type I error is much worse than making a type II, so most statisticians focus on just avoiding type I errors.
572304
Not accurate or worth it.\n\nThis is a simple/relatively cheap test to have done at the doctor's office.  It's WAY more worth it than saving just a few bucks for inaccurate results from a home test.
610087
The lab values you have given are not that far off from normal.  Don't jump the gun thinking you have something majorly wrong, talk to your doc!!!
11400
The Kjeldahl method for determining the Crude protein (CP) in a specimen is usually reported as percent CP in the sample.  According to most Kjeldahl procedures the following is typcal:\n“An acceptable average standard deviation among replicated analyses for crude protein ranges from about ±0.10 for samples with 10% CP to ±0.20 for samples with 20% CP, which results in warning limits (2s) ranging from ±0.20 to 0.40 and control limits (3s) ranging from ±0.30 to 0.60.”\nThe 0.10 is about 1% for the 10 percent specimen.  Modern methods are more accurate,
685395
Depends on the situation.\n\nFor example, you want to decide whether a batch of products is defective.  If your test come out to be significant but actually not (type I error), you have thrown away a perfectly fine batch of products.\n\nOn the other hand, for the same batch of products, if your test come out to be not significant but actually the batch is defective (type II error), then you have accepted a bad lot of products. \n\nSo in summary, type I error wastes money, type II error saves money but consumers don't like it.  Use similar analysis in your case.
700733
one extreme is immediatley-usually after a month or so-if you think you are, find out asap! a neg result does not always mean negative, so try it again later. Many test kits come with two for that reason!
980950
Hey Hep carrier, let's not get into a tizzy. Hep C is NOT the only strain of viral hep in existence.\n\nAs it so happens, Hepatitis B sometimes does cross react with HIV on the ELISA test. btw, the previous asker gave a FABULOUS explanation of how the tests work. Before we had HIV testing, CDC officials debated using the Hep B test as preliminary test for HIV, despite lower sensitivity to HIV.\n\nThe root of cross-reactivity is in the concepts of sensitivity and specificity. These are measures of test accuracy.\n\nA highly sensitive test picks up a lot of positives, including false positives. A highly specific test eliminates a lot of negatives, including a lot of false negatives.\n\nSo ideally, you want a test that is highly specific and sensitive, but balanced enough so that you pick up all the true positives and rule out all the true negatives.\n\nThe HIV ELISA test is very sensitive, so it picks up a lot of false positives. Because the immunoblot is expensive and time-consuming to run (though highly accurate), it's used as a back up to the quick and easy ELISA.
95550
r - standard error = the significance of the estimate
571081
testing is that this software contain any error or not.\n\nbut Quality Center always looks that this software Work's Fine , Faster & also on given requiremens or not.
438145
Chisq test for proportional differences or a One-way Chi-sq same thing though.  The difference in your proportions +/- 1.96 x standard error.
1142045
False positive are common among HIV test and your body chemistry may bring up another false positve. \n\nI doubt you have anything to worry about but your stress is real, I freak out when the cholestarol levels are slighty high this is HIV/AIDS of course paranoia is required.
311850
The stat isn't 50% but it's something closer to 25%. Either way - it's not good. \n\nYou should always assume that the people you have sex with are positive, unless they show you the test results from YESTERDAY saying they're negative - and they haven't had sex with a soul on earth since! :)\n\nIt would be great if more people were routinely tested, but there's so much of a stigma surrounding it, I'm afraid it's just not practical to expect it from people.