No.
The average of the deviations, or mean deviation, will always be zero.
The standard deviation is the average squared deviation which is usually non-zero.
Are you talking of this in means of Statistics? If you are, then the variation from the mean is measured in standard deviation.
No. A small standard deviation with a large mean will yield points further from the mean than a large standard deviation of a small mean. Standard deviation is best thought of as spread or dispersion.
The mean of a distribution is a measure of central tendency, representing the average value of the data points. In this case, the mean is 2.89. The standard deviation, which measures the dispersion of data points around the mean, is missing from the question. The standard deviation provides information about the spread of data points and how closely they cluster around the mean.
Standard deviation is a measure of variation from the mean of a data set. 1 standard deviation from the mean (which is usually + and - from mean) contains 68% of the data.
Relative dispersion = coefficient of variation = (9000/45000)(100) = 20.
Are you talking of this in means of Statistics? If you are, then the variation from the mean is measured in standard deviation.
These measures are calculated for the comparison of dispersion in two or more than two sets of observations. These measures are free of the units in which the original data is measured. If the original data is in dollar or kilometers, we do not use these units with relative measure of dispersion. These measures are a sort of ratio and are called coefficients. Each absolute measure of dispersion can be converted into its relative measure. Thus the relative measures of dispersion are:Coefficient of Range or Coefficient of Dispersion.Coefficient of Quartile Deviation or Quartile Coefficient of Dispersion.Coefficient of Mean Deviation or Mean Deviation of Dispersion.Coefficient of Standard Deviation or Standard Coefficient of Dispersion.Coefficient of Variation (a special case of Standard Coefficient of Dispersion)
No. A small standard deviation with a large mean will yield points further from the mean than a large standard deviation of a small mean. Standard deviation is best thought of as spread or dispersion.
The Absolute Measure of dispersion is basically the measure of variation from the mean such as standard deviation. On the other hand the relative measure of dispersion is basically the position of a certain variable with reference to or as compared with the other variables. Such as the percentiles or the z-score.
Standard deviation (SD) is a measure of the amount of variation or dispersion in a set of values. It quantifies how spread out the values in a data set are from the mean. A larger standard deviation indicates greater variability, while a smaller standard deviation indicates more consistency.
The units of dispersion are dependent on the units of the data being measured. Common measures of dispersion include variance and standard deviation, which have square units and the same units as the data being measured, respectively. Another measure, such as the coefficient of variation, is a unitless measure of dispersion relative to the mean.
It is not. And that is because the mean deviation of ANY variable is 0 and you cannot divide by 0.
Standard deviation is a measure of variation from the mean of a data set. 1 standard deviation from the mean (which is usually + and - from mean) contains 68% of the data.
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Standard deviation is a measure of the scatter or dispersion of the data. Two sets of data can have the same mean, but different standard deviations. The dataset with the higher standard deviation will generally have values that are more scattered. We generally look at the standard deviation in relation to the mean. If the standard deviation is much smaller than the mean, we may consider that the data has low dipersion. If the standard deviation is much higher than the mean, it may indicate the dataset has high dispersion A second cause is an outlier, a value that is very different from the data. Sometimes it is a mistake. I will give you an example. Suppose I am measuring people's height, and I record all data in meters, except on height which I record in millimeters- 1000 times higher. This may cause an erroneous mean and standard deviation to be calculated.
Relative dispersion = coefficient of variation = (9000/45000)(100) = 20.
Yes, a standard deviation of 4.34 can be correct. Standard deviation is a measure of dispersion or variability in a data set. It represents the average amount by which individual data points deviate from the mean. Therefore, a standard deviation of 4.34 simply indicates that there is some variability in the data, with data points on average deviating by 4.34 units from the mean.