hello, does anyone know what is SLOVIN'S FORMULA USED IN DETERMINATION OF SAMPLE SIZE?
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Try this ... has nothing to do with Chemestry though.
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some formulas that can help :\nhttp://www.eskridgeinc.com/support/formulas.html
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I think you are talking about that book:\n\nOldrich Vasicek, "A test for normality based on sample entropy," Journal of the Royal Statistical Society, Series B, vol. 38, no. 1, pp. 54--59, 1976.\n\nI couldn't find a copy of this article, but it is easy to find a lot of other articles that are refer to it.\nHope, one of them will help you.\n\nAlso I strongly recommend to use a Citeseer scientific article database (2nd attached link). It usually contains a lot of information.
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In my opinion, it doesn't take that large of a sample in order for the CLT to take effect.  Although in many classes, it is said that a sample larger than or equal to 25 is needed, that's not always necessary.\n\nIt does really depend on the shape of the population, though.  If the population is roughly symmetric, then a sample of 10 or possibly less might be sufficient.  If not, if the population is skewed and/or has outliers, then a larger sample would be necessary.\n\nAn elemetary stats book by Weiss has some good guidelines.  I don't really remember what exactly they are, but, basically, you should look at the histogram and the normal qq plot of the of the data.  Look at the histogram is roughly symmetric, and the normal qq plot is roughly linear.\n\nOne of my instructors once usually looked at the estimates for the skewness and  the kurtosis to make the determination.  I think that if the skewness is less than 2 in absolute value and kurtosis is less than 2.5, then the t test would be OK.\n\nedit: (This is the email I sent you earlier)\nI said the skewness and the kurtosis is less than those numbers, not greater than.\n\nYou can use Jarque-Bera as well as looking at the skewness and kurtosis.\n\nAlso, when I say kurtosis, I mean that it is the regular kurtosis minus 3, since 3 is the kurtosis of the normal.\n\nIn terms of what is considered minimum, well, the absolute minimum number of values you need is 2 of course.  But again, that's only if the population is considered normal.\n\nThe CLT converges much faster in some populations versus others.  So sometimes 10 is enough, sometimes not.  That's why I didn't set a particular number.\n\nedit: Yes, nonparametric tests can be used for pretty much any sample size.  But again, the sample size in which you transfer from parametric to nonparametric should depnd on the shape of the population.\n\nAlso, alyxsylvr, you would know that standard errors typically do not depend on the sample size as compared to the population size; it depends on the absolute size of the sample.
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You need to be more specific.\n\nHowever:\nThere are secondary variables - unintentional variations other than those variable you are testing.  The way to avoid this, such as noise, is to either find a way to eliminate it or make it uniform.\n\nA large sample size will make an experiment more accurate.\n\nDouble-blind studies (one where the research assistant administering the test does not know what is being tested for and therefor cannot influence the results) are good.\n\nIn any case, I cannot really answer further unless I know more about the study.
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n = N/(1+Ne(e))\n\nWhere:\nn = number of samples\nN = total population\ne = margin of error\n\nI could not find the squared key on my board so i just manipulated it, however it is the same.
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http://cis.jhu.edu/~bruno/s06-311/\n\nhttp://www.eng.wayne.edu/page.php?id=1434\n\nhttp://web.njit.edu/~dhar/math333/fin333_sp02A.pdf#search='sample%20Probability%20and%20Statistics%20exam'
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I am pondering this question a bit. I might have a better answer for you, but here is a start:\n- - - - - - - - - - - -  - \n\nYour data is not exactly amenable to standard quantitative analysis. You can't really form an average, for example. \n\nYou can find the mode. The most common answer is, diet. So the mode of your data is "diet". \n\nThe data categories, as they stand, are not represented by ordinal numbers. So it is a bit hard to even describe the distribution, let alone the dispersion of the distribution. What you could do, is say that 35% preferred diet, and 65% did not. Then you could try to estimate the error bounds on these figures, given some assumptions. Or do a robust estimate using a jackknife or bootstrap.\n\n- - - - - -  - - \n\nHere is another idea. You might want to know the average number of people that are in a given category. That is, you might want to know how many people like a given type of soda. You have 5 categories, so the average is \n\n(20)/5=4\n\nThere are an average of 4 people in each category.\n\nNow you can find the standard deviation.\n\nFirst find an estimate of the variance V using the standard formula:\n\nV=(1/4)*[(1-4)^2 + (3-4)^2 +(3-4)^2 +(7-4)^2 +(6-4)^2 ]\nV=(1/4)*[9 + 1 + 1 + 9 +4 ]\nV=(1/4)*[24]\nV=6\n\nThen take the square root\n\nS=sqrt(V)= sqrt(6)\nS=sqrt(6) = 2.44948974\n\nThe average soft drink is liked by 4 people, but there is a standard deviation of about 2.4.\n\nSo one can say a soft drink is liked by 4 += 2.4 people (if we set the error bounds to be 1 standard deviation, but some people set it at 2).\n\nYou could even convert this to percentages. Then you could say that a soft drink is liked by 20% of the people, +- 12%.\n\nThe standard deviation assumes that the distribution is normal, that is a bell-shaped curve (aka Gaussian). However, it probably is not. A robust estimate of the dispersion of the dispersion can be formed just resampling methods, like the bootstrap or the jackknife. I have placed a link to these if you are interested, but I think it might be too advanced for your needs.\n\nAnother thing to do is to look at the median, and the median average deviation, or the median average deviation away from the median. A reference discusses these and other measures of dispersion if you are interested.\n\nI still feel that one might be able to do more with this. I will think some more and if I have any good ideas I will post them. I am not a statistician, so it is likely that one of them might have a much better suggestion right away.
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Just apply the formulas you gave and the definitions.\nNote that the mean of the measurements (usually mu) is L here.\n\nThe expected value of S(1) is, expanding and using the independence of L(1) and L(2), \n\nES(1) = E[0.5(L(1)+ L(2))]^2\n= (1/4){E[L(1)^2] + 2E[L(1)L(2)] + E[L(2)^2]}\n= (1/4){...............+ 2E[L(1)]E[L(2)] +  ........}\n= (1/4)[(sigma^2+L^2) + 2L^2 + (sigma^2+L^2)]\n= L^2 + sigma^2/2 = A + (1/2)sigma^2\n\nso S(1) is biased upwards (as an estimator of A).\n\nSimilarly \n\nES(2) = E[0.5(L^2(1) + L^2(2))] = (1/2){E[L(1)^2] + E[L(2)^2]}\n= (1/2)[(sigma^2+L^2) + (sigma^2+L^2)] = L^2 + sigma^2\n= A + sigma^2\n\nwhich is more biased tahn S(1).\nHow about S(3) = L(1)L(2)? Using the independence of L(1) and L(2), \n\nE[L(1)L(2)] = E[L(1)] E[L(2)] = L^2\n\nso S(3) is unbiased.
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For one-sample t-test, \nt = (sample mean - population mean) / estimated standard error. \n\nIn math notation, t = (X bar - mu not) / (s/sqrt(n))\n\nAssumptions: (normal population or sample size, n, is at least 30) and population standard deviation is unknown.\n\nThere are other variations of t-tests like two-sample(pooled and unpooled) and paired ...
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Try this: http://en.wikipedia.org/wiki/Confidence_interval
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I hope, this can help you:\n\nLa Barre and Lowy (2001): Improvements in methods for calculating virus titer estimating TCID50 and plaque assays. J. Virol. Methods, Vol. 96, pp. 107-126.\n\nThis article is free at www.sciencedirect.com
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See comment above.\n\nYou do need to do several trials at various concentrations though. You can then determine it through a linear regression.\n\nI also suggest that you consult a book on Analytical chemistry, because Beer's law has limitations. For example, it loses linearity at higher concentrations.
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For all the info you would want:\n\nhttp://www.sizesurvey.com/result.html
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Do a confidence interval calculation and see if the confidence level for the data you've collected is above that required for 98%.\n\nI can show you how but given your other questions, it's clear you haven't really done any work on this yet and you're just looking to get someone to do your homework for you.\n\nLet me know if you still can't do it after you've tried a while, and I might help you out.
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See:\n\nThe section, Sample Size For Proportions ("Large" Populations), in the following:\nhttp://www.wku.edu/~david.neal/statistics/misc/sampsize.html\n\n...or:\n\nhttp://www.stat.auckland.ac.nz/~wild/ChanceEnc/Ch08.psampsize.pdf
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With IR you can dilute your soln so you have a bit more of it and then stick it on a plate and check out the peaks. That can give you a good idea of what you have... or there is HNMR, and CNMR, but it really depends on how much you have of the substance. If you have a general idea of what it might be then check the PH and give it a good guess... or use an indicator that might form some kind of ppt. with the substance in the soln . I've found that a refractive index really isnt the way to go because it isnt as reliable as other tests. Gas chromotograpy and Thin layer chormotograpy both require a general idea of what you might be coming up with... GC can be sketchy and you dont want to loose any sample on a test that might not tell you much, same with TLC, you would haev to be careful with the slovent interaction with the "unknown" that you have.