define standard deviation?
542258
From http://www.robertniles.com/stats/stdev.shtml\n\nThe standard deviation is a statistic that tells you how tightly all the various examples are clustered around the mean in a set of data. When the examples are pretty tightly bunched together and the bell-shaped curve is steep, the standard deviation is small. When the examples are spread apart and the bell curve is relatively flat, that tells you you have a relatively large standard deviation.\n\n----------\n\nFrom http://en.wikipedia.org/wiki/Standard_deviation\n\nIn probability and statistics, the standard deviation is the most common measure of statistical dispersion. Simply put, standard deviation measures how spread out the values in a data set are. More precisely, it is a measure of the average difference between the values of the data in the set. If the data points are all similar, then the standard deviation will be low (closer to zero). If the data points are highly variable, then the standard variation is high (further from zero).
373106
The sampling error in the mean estimate is reduced by increasing the the size of the sample.\n\nThe standard deviation of the sample does not change as one poster suggested.  The standard deviation and variance should stay roughly the same, independent of sample size because they are measures of the population.\n\nHowever, the standard deviation of the sampling distribution -- the distribution of sample means from repeated samples -- is called the standard error.\n\nA property of the standard error is that it may be approximated by the stdev / sqrt(N) where N is the sample size.  This says that the standard error is proportional to 1/sqrt(N).  An implication of this is that you need to have a sample size that is 4X larger than your current sample size to get a standard error that is 1/2 the current standard error.
967643
z score= (n-mean)/standard deviation\nstandard deviation = square root of variance\n\nz score = (50-24)/ 5\n            = 26/5\n            = 5.2\n\nThe z score of the data is 5.2, meaning n is 5.2 standard deviations from the mean.
695289
standard deviation of intercept is a quantity related to the precision of the estimated intercept of the fitted line. It can be calculated by the formula:\n\nS_a = (S_b)*(sqrt( (sum(x_j)^2)/m )) \n\nWhere, S_b is standard deviation of slope\n\nS_b = sqrt( ((m*(s_y)^^2) / [m*(sum(x_j)^2) -(sum x_j)^2] )\n\ns_y is the residual standard deviation.\n\na and b are estimated parameters of the line y = a + bx +err.\n\n[Here, by S_b, I mean S with suffix b, Sorry but its a bit difficult to type mathematics with only text.]\nI hope , I answered your question.
187632
The relative average deviation is the average deviation divided by the average of the measurements....lol\n\nIf that doesn't help much, check out the link below.\n\nMatt
975814
Variance = (∑x²)/n - (∑x/n)² \n= (4 + 25 + 64)/3 - [(2 + 5 + 8)/3]²\n= 31 - 25\n= 6\nThus standard deviation = √(variance)\n= √6
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Mean is the average, when adding all numbers then dividing by the amount of numbers given.\n\nMedian is the numbers in the middle average - 8
659324
Statistical:\nOf, relating to, or employing statistics or the principles of statistics.\n\nThe word statistics is also the plural of statistic (singular), which refers to the result of applying a statistical algorithm to a set of data.
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it is the average amount that each sample (or reading number) deviates from the mean (average). It is a measure of how widely the data is scattered away from the mean.\n\nThis is highly simplified, you might want to look on wikipedia for a better explaination. (it involves square roots and other nasty maths)
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What you're looking for is the standard deviation.  If the data values are X and the mean is XBAR, then\n\nS.D. = sqrt{[SUM(X - XBAR)^2] / n}\n\nn is the number of data items, and SUM means to add up the squares of the differences between the data values and the mean.\n\nThe lower the standard deviation, the more tightly packed the data.  If the data is normally distributed, about 68% of the data will lie within one standard deviation of the mean, and 95% will be within two standard deviations.\n\n[Edit:  In light of the previous answers, I should add that the standard deviation is the square root of the variance.]
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Take each of your values, and then subtract the mean from them.  E.g., 70 - 56 = 14, 60 - 56 = 4, etc.\n\nNow take each of these answers and square it.  Half of the starting numbers will be negative (eg 30 - 56 = -26) but once you square all of these numbers, they should all be positive.\n\nNow you'll have 7 positive numbers. Add them all together and divide the answer by 7.  Finally, take the square root of that value, and that's the standard deviation.\n\nSo 14, 4, -6, 4, 4, 9, and -26\nbecome 196, 16, 36, 16, 16, 81, and 676\nSummed together gives 1037\ndivided by 7 is ~148.14\nSquare-rooted is ~12.2\n\nThe standard deviation is 12.2.\n\nNOTE: the rt11guru answer that follows is based on a technique used in some larger sample sets where 1 is subtracted from the number of entries, ie the sum is divided by 7-1=6 rather than 7.  That is called the *sample standard deviation*, as shown in the link.
905827
If a random variable has a standard deviation of s and you take N samples, then the standard deviation of the sample mean is s/sqrt(N).\n\nIn your case you assume that the standard deviation of the random variable is close to 50 because you have such a huge number of samples. Now if you turn around and only look at 30 samples then the standard deviation will be\n50/sqrt(30).
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2 standard deviations above average (100).\n\nThat means 130 or 142 depending on the IQ test(s) being used.\n\nI joined a few years ago, mostly to prove I could, but found the whole thing pretty dull.
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"In probability and statistics, the standard deviation of a probability distribution, random variable, or population or multiset of values is defined as the square root of the variance.\n\nThe standard deviation is measured in the same units as the values of the population. For a population of distances in meters, the standard deviation is also measured in meters, whereas the variance is measured in square meters."\n\n\nIn essence, standard deviation is used for categorizing groups.\nFor example, the average IQ is 100, the standard deviation to the left would be 85, and deviation to the right would be 115. Two left standard deviations would be 60, two right standard deviations would be 130.\nSame applies to SAT scores, statistics, polls, etc.
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There are actually many different types of "mean." The most common "mean"  is the arithmetic mean, also called the average in everyday language.\n\nCalculating the arithmetic mean is simple: Add all the values in the sample together and divide by the total number of values in the sample.\n\nThe arithmetic mean is often used to describe data. The problem is that it doesn't always necessarily give the correct picture.  Sometimes it is better to use the median, which is the number found exactly in the middle of the set of values, or the mode, which is the number most frequently reported. It depends on the data involved and the question you are asking about the data.\n\nWhat this all comes down to is: the mean is a measure of central tendency (as are the median and the mode).\n\nThe other two most common means are the geometric mean and the harmonic mean. If you are interested in leanring more, visit Wikipedia.
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The coefficient of variation just compares the standard deviation to the mean.  It is calculated S.D./ Mean sometimes * 100.  It is difficult to compare standard deviations of variables where the units are not the same.  For example if one variable had units in million of dollars and another in cents how do you compare their variability?  One way to compare them and get an idea of their variability is to use the C.V.
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It's easiest to understand the equation for the standard deviation by thinking of it as the square root of the variance.  So really, you need to know the  equation for the variance.  However, there are equations for standard deviation that don't take that into account.  Either way, there are fair explainations of the meaning and equations for standard deviation and variance at the sources listed below.  \n\nFor the simplest explaination of equations for standard deviation, read the section titled "Definition and shortcut calculation of standard deviation".[1]