whats standard deviation in maths?
101856
Standard deviation is a measure of the variability in a sample.  A small standard deviation means that most observations are clost to the mean (average).  The standard deviation is the square root of the variance.  The variance is calculated by taking the difference between each observation and the mean, squaring it, summing for all observations, then dividing by the number of observations less one.\n\nwith m = mean and X1 ... Xn being the observations:\n\nvariance: V = [(X1-m)^2 + (X2-m)^2 ... + (Xn-m)^2] / (n-1)\nstandard deviation: sigma = sqrt(V)
437434
Range: 19\nStdev: 6.27\n\nRange is just the max value minus the min value.  To learn more about calculating standard deviation, visit http://en.wikipedia.org/wiki/Standard_deviation\n\nSo, I saw someone got a different standard deviation than me, so I checked it again on Excel and I still got 6.27.  Instead of dividing by 7, I divided by 6 (n-1).\n\nIf these data are a sample of a larger population, then you divide by 6 (n-1) to estimate the stdev of the population, so the stdev is 6.27.  If you just want to know the stdev of those numbers, then you divide by 7 (n) so the stdev is 5.81.
280200
If a random sample size of 25 corresponds to a standard deviation of 30, then you can expect a random sample of size 25N to roughly correspond to a standard deviation of 30 / sqrt(N).\n\nIf 99% of the area under the bell curve is between -Z and +Z standard deviations, then you want 30 Z / sqrt(N) to be less than or equal to 0.5.\n\nMaybe a little more because of the variation of the estimated standard deviation from the true standard deviation.\n\nAlso check that the theory and the measurements both suggest it is appropriate to use the normal or Gaussian distribution.
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.
1038416
1 standard deviation range is\n\n74-13=61\nto\n74+13=87\n\nSo any score between 61-87 is 1 standard deviation of the mean 74. So a 62 is in that range.
551009
If you can't answer this, you have no idea what the empirical rule is.\n\nAbout 68% of all values fall within 1 standard deviation of the mean for the normal distribution.
954856
mean is 85/3, or 28.33333......\n\ni dont know, its just always been that way
20676
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]
149503
Right, so feed the numbers into your calculator and press the SD button.\n\nOr enter it into Excel and use teh standard deviation function.
826326
A greek letter..... it can mean population standard deviation. Not sure of other meanings.
874169
There appear to be two different definitions of the standard error.\n\nThe standard error of a sample of sample size n is the sample's standard deviation divided by sqrt(n). It therefore estimates the standard deviation of the sample mean based on the population mean (Press et al. 1992, p. 465). Note that while this definition makes no reference to a normal distribution, many uses of this quantity implicitly assume such a distribution.\n\nThe standard error of an estimate may also be defined as the square root of the estimated error variance sigma^^^2 of the quantity,\ns_e=sqrt(sigma^^^2)\n\n\n\n\n    The standard error is the estimated standard deviation of a statistic. The formula depends on what statistic you are talking about. For example, the standard error of a sample mean is just the sample standard deviation divided by the square root of the sample size. For more complicated statistics, the standard error is also more complicated.\n\n    You can use the standard error in two ways. First, the statistic divided by the standard error gives you a way of testing whether the parameter that your statistic is estimating equals zero. You compare this ratio to a t-distribution. In most contexts, the ratio of the statistic divided by its standard error is called a Wald test. For many simple applications, you can also call this a t-test.\n\n    A second application of the standard error is the production of confidence intervals. You can get a crude confidence interval by taking your statistic plus or minus two standard errors. A more precise confidence interval would use percentiles from the t-distribution.\n\n    There are a lot of subtleties in the use of the standard error, especially in more complex problems. Sometimes, for example, the standard error applies not to the statistic itself, but to the logarithm of that statistic. For example, a logistic regression model will compute an odds ratio for your data, but the standard error refers not to the odds ratio, but to the log odds ratio. In this situation, you need to compute the confidence interval on the log scale and then transform the results back to the original scale of measurement.\n&#92;\n\ns
120843
R^2 (root mean square) is just a statistical measure of how well your data lines up. Anything over .99 is near perfect.
29423
Standard deviation tells you how spread out a set of numbers is.  A small standard deviation means that the numbers are close together.  A large one says that they're far apart.\n\nIt's often used as a measure of distance away from the mean.  In a normal distribution, about 68% of numbers are within one standard deviation from the mean.  For example, if you have a normal distribution with a mean of 50 and a standard dev of 10, then about 68% of the numbers are between 40 and 60.  And about 95% of the numbers are within two standard deviations, or between 30 and 70.
825321
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)
889683
There is, it is called Standarad Deviation, if you have a decent Scientific Calculator it shouyld show how to use it.
305208
oh puke, what a pain in the rear.  i don't envy you.  like the other dude said use excel, but if you have to do it on paper, here's the formula:\n \nIn other words, the standard deviation of a discrete uniform random variable X can be calculated as follows:\n\n1.  find the mean of the set of numbers\n2.  subtract the mean from the first number\n3.  square the difference between that number and the mean\n4.  repeat steps 2 and 3 for all numbers\n5.  add all of the differences from step 3\n6.  dvide this number by the number of numbers you had in the set\n7.  take the square root of this\n\n\nthat's the standard deviation.
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".