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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.

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โˆ™ 2013-10-13 21:01:50
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Q: Does standard deviation and mean deviation measure dispersion the same?
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Related questions

Which measure of dispersion represents variation from the mean?

Are you talking of this in means of Statistics? If you are, then the variation from the mean is measured in standard deviation.


Is the standard deviation best thought of as the distance from the mean?

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.


What is relative measure?

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)


What is difference between absolute measure of dispersion and relative measures of 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.


What does one standard deviation 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.


Why standard deviation scores over mean deviation as a more accurate measures of dispersion?

It is not. And that is because the mean deviation of ANY variable is 0 and you cannot divide by 0.


Why do you seldom use the mean or average deviation as a measure of dispersion?

because of grace severo


What is the relative dispersion with the mean of 45000 and a standard deviation of 9000?

Relative dispersion = coefficient of variation = (9000/45000)(100) = 20.


What determines the standard deviation to be high?

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.


What are the common measures of dispersion?

Range, standard deviation, variance, root mean square, interquartile range


Which measure of varartion is preferred when the mean is used as the measure of center?

standard deviation


What does the mean absolute deviation tell you about a set of data?

It is a measure of the spread or dispersion of the data.


What is the standard deviation?

The standard deviation of a set of data is a measure of the spread of the observations. It is the square root of the mean squared deviations from the mean of the data.


Why use standard deviation In what situations is it special?

I will restate your question as "Why are the mean and standard deviation of a sample so frequently calculated?". The standard deviation is a measure of the dispersion of the data. It certainly is not the only measure, as the range of a dataset is also a measure of dispersion and is more easily calculated. Similarly, some prefer a plot of the quartiles of the data, again to show data dispersal.t Standard deviation and the mean are needed when we want to infer certain information about the population such as confidence limits from a sample. These statistics are also used in establishing the size of the sample we need to take to improve our estimates of the population. Finally, these statistics enable us to test hypothesis with a certain degree of certainty based on our data. All this stems from the concept that there is a theoretical sampling distribution for the statistics we calculate, such as a proportion, mean or standard deviation. In general, the mean or proportion has either a normal or t distribution. Finally, the measures of dispersion will only be valid, be it range, quantiles or standard deviation, require observations which are independent of each other. This is the basis of random sampling.


What is the difference between standard error of sample mean and sample standard deviation?

Standard error A statistical measure of the dispersion of a set of values. The standard error provides an estimation of the extent to which the mean of a given set of scores drawn from a sample differs from the true mean score of the whole population. It should be applied only to interval-level measures. Standard deviation A measure of the dispersion of a set of data from its mean. The more spread apart the data is, the higher the deviation,is defined as follows: Standard error x sqrt(n) = Standard deviation Which means that Std Dev is bigger than Std err Also, Std Dev refers to a bigger sample, while Std err refers to a smaller sample


What do the variance and the standard deviation measure?

They are measures of the spread of distributions about their mean.


Which measure of variation is appropriate when using the mean?

The variance or standard deviation.


What is mean and standard deviation?

They are statistical measures. For a set of observations of some random variable the mean is a measure of central tendency: a kind of measure which tells you around what value the observations are. The standard deviation is a measure of the spread around the mean.


What do you mean by measures of central tendency and dispersion?

Common measures of central tendency are the mean, median, mode. Common measures of dispersion are range, interquartile range, variance, standard deviation.


What are are measures of variability or dispersion within a set of data?

Some measures:Range,Interquartile range,Interpercentile ranges,Mean absolute deviation,Variance,Standard deviation.Some measures:Range,Interquartile range,Interpercentile ranges,Mean absolute deviation,Variance,Standard deviation.Some measures:Range,Interquartile range,Interpercentile ranges,Mean absolute deviation,Variance,Standard deviation.Some measures:Range,Interquartile range,Interpercentile ranges,Mean absolute deviation,Variance,Standard deviation.


Will increasing the frequency of scores in the tails of a distribution effect the standard deviation How or Why?

Yes. It will increase the standard deviation. You are increasing the number of events that are further away from the mean, and the standard deviation is a measure of how far away the events are from the mean.


If quartile deviation is 24. find mean deviation and standard deviation?

Information is not sufficient to find mean deviation and standard deviation.


What is the purpose of finding the standard deviation of a data set?

The purpose of obtaining the standard deviation is to measure the dispersion data has from the mean. Data sets can be widely dispersed, or narrowly dispersed. The standard deviation measures the degree of dispersion. Each standard deviation has a percentage probability that a single datum will fall within that distance from the mean. One standard deviation of a normal distribution contains 66.67% of all data in a particular data set. Therefore, any single datum in the data has a 66.67% chance of falling within one standard deviation from the mean. 95% of all data in the data set will fall within two standard deviations of the mean. So, how does this help us in the real world? Well, I will use the world of finance/investments to illustrate real world application. In finance, we use the standard deviation and variance to measure risk of a particular investment. Assume the mean is 15%. That would indicate that we expect to earn a 15% return on an investment. However, we never earn what we expect, so we use the standard deviation to measure the likelihood the expected return will fall away from that expected return (or mean). If the standard deviation is 2%, we have a 66.67% chance the return will actually be between 13% and 17%. We expect a 95% chance that the return on the investment will yield an 11% to 19% return. The larger the standard deviation, the greater the risk involved with a particular investment. That is a real world example of how we use the standard deviation to measure risk, and expected return on an investment.


What are importance of mean and standard deviation in the use of normal distribution?

For data sets having a normal distribution, the following properties depend on the mean and the standard deviation. This is known as the Empirical rule. About 68% of all values fall within 1 standard deviation of the mean About 95% of all values fall within 2 standard deviation of the mean About 99.7% of all values fall within 3 standard deviation of the mean. So given any value and given the mean and standard deviation, one can say right away where that value is compared to 60, 95 and 99 percent of the other values. The mean of the any distribution is a measure of centrality, but in case of the normal distribution, it is equal to the mode and median of the distribtion. The standard deviation is a measure of data dispersion or variability. In the case of the normal distribution, the mean and the standard deviation are the two parameters of the distribution, therefore they completely define the distribution. See: http://en.wikipedia.org/wiki/Normal_distribution


A standard normal distribution has a mean of and standard deviation of?

Mean 0, standard deviation 1.