Math and Arithmetic
Statistics

# How do you calculate standard deviation if I know variance?

123 ###### 2012-07-16 04:37:19

Standard deviation = Square root of variance.

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## Related Questions Standard deviation is how much a group deviates from the whole. In order to calculate standard deviation, you must know the mean. No. Well not exactly. The square of the standard deviation of a sample, when squared (s2) is an unbiased estimate of the variance of the population. I would not call it crude, but just an estimate. An estimate is an approximate value of the parameter of the population you would like to know (estimand) which in this case is the variance. The mean and standard deviation do not, by themselves, provide enough information to calculate probability. You also need to know the distribution of the variable in question.   We need the standard deviation to describe or know about the variation of the numbers. "Variance" and "Standard deviation" are numbers that describe a set of data that typically contains several numbers. Applied to a single number, neither of them has any meaning. -- The variance, standard deviation, and mean squared error of 7 are all zero. -- The mean, median, mode, average, max, min, RMS, and absolute value of 7 are all 7 . None of these facts tells you a thing about ' 7 ' that you didn't already know as soon as you found out that it was ' 7 '. A negative Z-Score corresponds to a negative standard deviation, i.e. an observation that is less than the mean, when the standard deviation is normalized so that the standard deviation is zero when the mean is zero. The standard deviation of a distribution is the average spread from the mean (average). If I told you I had a distribution of data with average 10000 and standard deviation 10, you'd know that most of the data is close to the middle. If I told you I had a distrubtion of data with average 10000 and standard deviation 3000, you'd know that the data in this distribution is much more spread out. dhaussling@gmail.com You also know that x is 1.036 times the standard deviation of the variable above its mean. Anything more than that would require further information about the mean and/or the variance of the variable. It depends on the data. The standard deviation takes account of each value, therefore it is necessary to know the values to find the sd. The idea is to know how much the values "spread out" from the average. If it is possible to assume normality, simply convert the desired score to a z-score, and look up the probability for that. It is defined as the positive square root of the mean of the squared deviations from mean. The square of S.D is called variance. The standard deviation is used as a measure of the variance of a measurement within a group of objects. In essence, it is the average difference between the measurement of any one object and the mean measurement for the group. For example, if the average measured weight of brown bears is 140kg (265lbs) and the standard deviation of weights among brown bears is 5kg (11lbs), then any particular, individual brown bear is likely to weight between 135-145kg (254-276lbs), and very likely to weight between 130-150kg (243-287lbs). It's impossible to know the weight of an individual bear just by looking at the mean weight for all bears, but the standard deviation tells you what range of weights the weight of an individual bear will fall in. The relative standard deviation is the absolute value of the ration of the sample mean to the sample standard deviation. This value appears to be quite small; however, without comparative data it is difficult to know what to make of it. In some contexts it might even be considered large.    The standard deviation would generally decrease because the large the sample size is, the more we know about the population, so we can be more exact in our measurements.   Standard deviation helps business understand the research they have done on their potential customers. If the information deviates by one, then they know that they can rely on the information more so than research that deviates to standards deviations away from the mean. It depends what you're asking. The question is extremely unclear. Accuracy of what exactly? Even in the realm of statistics an entire book could be written to address such an ambiguous question (to answer a myriad of possible questions). If you simply are asking what the relationship between the probability that something will occur given the know distribution of outcomes (such as a normal distribution), the mean of that that distribution, and the the standard deviation, then the standard deviation as a represents the spread of the curve of probability. This means that if you had a cure where 0 was the mean, and 3 was the standard deviation, the likelihood of observing a value of 12 (or -12) would be likely inaccurate if that was your prediction. However, if you had a mean of 0 and a standard deviation of 100, the likelihood of observing of a 12 (or -12) would be quite likely. This is simply because the standard deviation provides a simple representation of the horizontal spread of probability on the x-axis. Schedule variance (SV) - This is the deviation of the performed schedule from the planned schedule in terms of cost. No confusion is allowed here because you already know that the schedule can be translated to cost. SV is calculated as the difference between EV and PV, as shown in the formula here:SV = EV - PV  