Standard deviation is the variance from the mean of the data.
The mean is the average value and the standard deviation is the variation from the mean value.
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A normal distribution with a mean of 65 and a standard deviation of 2.5 would have 95% of the population being between 60 and 70, i.e. +/- two standard deviations.
The area within the normal curve between -1 standard deviation (SD) and +1 SD is approximately 68%. This means that about 68% of the data falls within one standard deviation of the mean in a normal distribution.
Standard deviation doesn't have to be between 0 and 1.
Standard deviation is the square root of the variance.
Standard deviation is the variance from the mean of the data.
Standard error of the mean (SEM) and standard deviation of the mean is the same thing. However, standard deviation is not the same as the SEM. To obtain SEM from the standard deviation, divide the standard deviation by the square root of the sample size.
The distance between the middle and the inflection point is the standard deviation.
The standard deviation is the square root of the variance.
The mean is the average value and the standard deviation is the variation from the mean value.
The 68-95-99.7 rule, or empirical rule, says this:for a normal distribution almost all values lie within 3 standard deviations of the mean.this means that approximately 68% of the values lie within 1 standard deviation of the mean (or between the mean minus 1 times the standard deviation, and the mean plus 1 times the standard deviation). In statistical notation, this is represented as: μ ± σ.And approximately 95% of the values lie within 2 standard deviations of the mean (or between the mean minus 2 times the standard deviation, and the mean plus 2 times the standard deviation). The statistical notation for this is: μ ± 2σ.Almost all (actually, 99.7%) of the values lie within 3 standard deviations of the mean (or between the mean minus 3 times the standard deviation and the mean plus 3 times the standard deviation). Statisticians use the following notation to represent this: μ ± 3σ.(www.wikipedia.org)
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The more precise a result, the smaller will be the standard deviation of the data the result is based upon.
Standard error is the difference between a researcher's actual findings and their expected findings. Standard error measures the accuracy of one's predictions. Standard deviation is the difference between the results of one's experiment as compared with other results within that experiment. Standard deviation is used to measure the consistency of one's experiment.
A normal distribution with a mean of 65 and a standard deviation of 2.5 would have 95% of the population being between 60 and 70, i.e. +/- two standard deviations.