The standard deviation.z-score of a value=(that value minus the mean)/(standard deviation)
Let sigma = standard deviation. Standard error (of the sample mean) = sigma / square root of (n), where n is the sample size. Since you are dividing the standard deviation by a positive number greater than 1, the standard error is always smaller than the standard deviation.
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 is the standard deviation! Its calculation requires no assumption.
z-score of a value=(that value minus the mean)/(standard deviation)
It does not indicate anything if the mean is greater than the standard deviation.
The mean is the average value and the standard deviation is the variation from the mean value.
No. The standard deviation is not exactly a value but rather how far a score deviates from the mean.
No standard deviation can not be bigger than maximum and minimum values.
Standard deviation in statistics refers to how much deviation there is from the average or mean value. Sample deviation refers to the data that was collected from a smaller pool than the population.
If the minimum value is the minimum observed value then it indicates that the distribution goes below the minimum observed value.If the minimum value is the minimum defined for the distribution then it indicates thatthe data do not come from the proposed distribution,estimates for the mean or standard deviation are incorrect, oryou have got a sample which is atypical.
No. The expected value is the mean!
There is 1) standard deviation, 2) mean deviation and 3) mean absolute deviation. The standard deviation is calculated most of the time. If our objective is to estimate the variance of the overall population from a representative random sample, then it has been shown theoretically that the standard deviation is the best estimate (most efficient). The mean deviation is calculated by first calculating the mean of the data and then calculating the deviation (value - mean) for each value. If we then sum these deviations, we calculate the mean deviation which will always be zero. So this statistic has little value. The individual deviations may however be of interest. See related link. To obtain the means absolute deviation (MAD), we sum the absolute value of the individual deviations. We will obtain a value that is similar to the standard deviation, a measure of dispersal of the data values. The MAD may be transformed to a standard deviation, if the distribution is known. The MAD has been shown to be less efficient in estimating the standard deviation, but a more robust estimator (not as influenced by erroneous data) as the standard deviation. See related link. Most of the time we use the standard deviation to provide the best estimate of the variance of the population.
A small standard deviation indicates that the data points in a dataset are close to the mean or average value. This suggests that the data is less spread out and more consistent, with less variability among the values. A small standard deviation may indicate that the data points are clustered around the mean.
Standard Deviation = (principal value of) the square root of Variance. So SD = 10.
A standard deviation of zero means that all the data points are the same value.
The standard deviation.z-score of a value=(that value minus the mean)/(standard deviation)