Can someone explain "the error of the estimate becomes larger than the estimate." pleeeeease?
353190
When estimating some value, there will always be an error due to innaccuracies and inconsistencies in the measurement and calculation stages.\n\nThe size of the error explains the range of possible values that the actual measurement falls in. So a measurement of 30 ± 2 means the range of possible values is 28 to 32.\n\nIf the error of the estimate becomes larger than the estimate, then the estimate is useless because it hasn't narrowed down the range of possible values.\n\n30 ± 30 means a range from 0 to 60. This effectively means the value could be anything from nothing to a large amount, which is not a useful estimation of the value.
303836
Nope,\n\n   The log function blows up to infinity.  (If you don't believe this, give me a value for y=f(x) = K * log(x).  Ha ha! I can give you a value for x such that f(x) is greater than the value you gave me! )\n\n  Your pal from Calc I\n   cuntsucker
863028
Is that really the question?  Prob of a normal variable being greater than the mean?  It's just 0.5.  What's the real question, and what has Z to do with it?
821523
It means that while the surface is enlarging, it enlarges by being squared. the volume is cubed. W*L vs L*W*Height or depth, or whatever. So the surface increase can not keep up with volume increase. S^2/V^3 Talk about an improper fraction!
876541
something is wrong in the way the problem is stated\nboth the answers are wrong, according to me.\nif you differentiate -19800-50.01x  you will get -50.01 and that cannot be equated to zero
415757
I hate to tell you, but I don't think there is an algebraic method for arriving at a value for x. Sometimes, this can be the case, in which case all you can do is find a numeric approximation, here around x=1.0587...\n\nI'll see if another user can prove me wrong, but I'm going to go with there's no way to solve this problem using analytic methods.
444813
A quotation by Harvey J Gold, Author of Mathematical Modelling of Biological Systems,  may go towards assisting your analysis of the situation:\n\n" The result of a mathematical development should be continuously checked against one's own intuition about what constitutes reasonable biological behaviour. Whenever such a check reveals disagreement, the following possibilities must be considered:\n\na : A mistake has been made in the formal mathematical development\n\nb: The starting assumptions are incorrect and/or constitute a too drastic oversimplification\n\nc: One's own intuition about the biological field is inadequately developed.\n\nd: A penetrating new principle has been discovered. "\n\nDo let me know whether you found meaning in the above, and if you did analyse your model based on the checks recommended by him, what among a,b,c,d was the reason of the discrepancy. Of course this is assuming that you have already established the discrepancy beyond doubt.
500764
Let me try to give examples for when you use one, and when you use the \nother.\n\nThe profit of Company A, SYZO Ltd., has grown over the last three \nyears by 10 million, 12 million, and 14 million dollars. It is \nappropriate to say that it has grown by an average of 12 million \ndollars yearly, for which we use the arithmetic mean.\n\nThe profit of Company B, OZYS Ltd., has grown the over last three \nyears by 2.5%, 3%, and 3.5%. Here we cannot use the arithmetic mean \nand say that the average growth was 3%. Why not?\n\nSuppose that Company B, OZYS Ltd., started with a 100-million-dollar \nprofit. Three years later it will have become:\n\n     $100,000,000 * 1.025 * 1.03 * 1.035 = $109,270,125\n\nThis is less than a yearly increase of 3% would yield, since:\n\n     $100,000,000 * 1.03 * 1.03 * 1.03   = $109,272,700\n\nHere we see that we should use the geometric mean of the growth \nfactors 1.025, 1.03, and 1.035 to find the average percentage. That is \nalways less than the arithmetic mean would yield.\n\nI hope this helps.
991856
It is called WRONG. If A is much larger than B, then A+B/A is about A unless you meant (A+B)/A, in which case the result is about 1. In neither case do you get A/B.
820314
Standard error is standard deviation divided by n^1/2. In other words, it estimates the standard deviation of the sample mean _based on the population mean_.
1117620
Depends on what data the survey collects. If it is numerical data that can be analyzed, then both errors can occur in either a survey or an experiment.\n\nIn the mathematical subfield of numerical analysis the approximation error in some data is the discrepancy between an exact value and some approximation to it. \n\nAn approximation error can occur because the measurement of the data is not precise (due to the instruments), or we use approximations instead of the real data (e.g., 3.14 instead of π). \nOne commonly distinguishes between the relative error and the absolute error.\n\nThe numerical stability of an algorithm in numerical analysis indicates how the error is propagated by the algorithm.
418331
Possibly "statistical fluctuations in charge generation" as mentioned in ref. 1. From ref. 2, which refers to "fundamental limits imposed by charge generation statistics", I get the impression that these variations may be an irreducible error source.
307198
It is linear on intervals, on (- infinite, 0) and on (0, +infinite) but not on the whole R. \n\nTry plotting it, you get a reversed V, not a straight line.
809077
I would assume that one of your .01s in column A is something like 0.012.\n\nChange your number  format to General and see what happens.
1100234
There are two types of errors. One human error another instrumental error. \nWe know the accuracy of measurement of instruments. \n\nAny error in the result must be within this instrumetal error.\n\nHuman error must be eliminated in total. \n\nThis can be done only by repating the experiment.\n\nAny result which is not within the instrumetal error must be totaly rejected and we find the mean value of the results which are within the instrumental errors
437525
When you compute a mean from a sample, you are estimating the mean of the population from which you sampled.  The population mean is sometimes refered to as the true mean and the mean you compute from your sample is called the sample mean.\n\nWith repeated sampling, you'll come up with a different sample mean each time.  This distribution of sample means is refered to as the sample distribution and it is approximately Normal in distribution.\n\nWhen you compute the standard error using the standard deviation and the sample size, you are estimating the variability in the sample mean.\n\nWhen you estimate confidence intervals, you are estimating the range of possible values where the population may lie relative to your sample mean.\n\nFor instance, your sample mean plus or minus 1.96 standard errors represents an estimate of the 95% confidence interval or range that may contain the population mean relative to your sample mean.  There is a 5% chance that the population mean is actually higher or lower than this range.\n\nLet's say you toss a coin twenty times and measure 9 heads and 11 tails.  The average number of coin tosses at 0.45 heads (which implies 0.55 tails).  The standard error estimate is 0.055343. Multipling the standard error estimate by 1.96 and adding and subtracting the result from the mean yields our 95% confidence interval. There is a 95% chance that the population mean for this process is between 0.341528 and 0.558472.
961030
in Statistics in theory there should zero measurements falling beyond the three standard deviations from the mean on either side. though in practical some values do fall in this range. I don't remember the exact statistical names of these values but i think they are referred to as "outliers".