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What is the Calculated Standard Deviation In The Following Data 13 12 9 15 11 16 17 8 12 7 12?
Wiki User
∙ 6y ago
It is 3.045
Wiki User
∙ 6y ago
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Can standard deviation be calculated for non normal data?
Standard deviation can be calculated using non-normal data, but isn't advised. You'll get abnormal results as the data isn't properly sorted, and the standard deviation will have a large window of accuracy.
Does variance and standard deviation assume nominal data?
No. Variance and standard deviation are dependent on, but calculated irrespective of the data. You do, of course, have to have some variation, otherwise, the variance and standard deviation will be zero.
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 a large standard deviation means?
A large standard deviation means that the data were spread out. It is relative whether or not you consider a standard deviation to be "large" or not, but a larger standard deviation always means that the data is more spread out than a smaller one. For example, if the mean was 60, and the standard deviation was 1, then this is a small standard deviation. The data is not spread out and a score of 74 or 43 would be highly unlikely, almost impossible. However, if the mean was 60 and the standard deviation was 20, then this would be a large standard deviation. The data is spread out more and a score of 74 or 43 wouldn't be odd or unusual at all.
What are the units of measurement of standard deviation?
Standard deviation has the same unit as the data set unit.
Can standard deviation be calculated for non normal data?
Standard deviation can be calculated using non-normal data, but isn't advised. You'll get abnormal results as the data isn't properly sorted, and the standard deviation will have a large window of accuracy.
Does variance and standard deviation assume nominal data?
No. Variance and standard deviation are dependent on, but calculated irrespective of the data. You do, of course, have to have some variation, otherwise, the variance and standard deviation will be zero.
What would it mean if a standard deviation was calculated to equal 0?
A standard deviation of zero means that all the data points are the same value.
What is the standard deviation of the following data set rounded to the nearest tenth 6040354539?
The standard deviation of a single number, as in this question, is 0.
What is the Standard deviation of a set of data in which all the data values are the same?
ZeroDetails:The "Standard Deviation" for ungrouped data can be calculated in the following steps:all the deviations (differences) from the arithmetic mean of the set of numbers are squared;the arithmetic mean of these squares is then calculated;the square root of the mean is the standard deviationAccordingly,The arithmetic mean of set of data of equal values is the value.All the deviations will be zero and their squares will be zerosThe mean of squares is zeroThe square root of zero is zero which equals the standard deion
What is standard deviation used for?
Standard deviation is a measure of the spread of data.
If the standard deviation is small the data is more dispersed?
No, if the standard deviation is small the data is less dispersed.
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 do we need the standard deviation?
The standard deviation is a measure of the spread of data.
Relation between mean and standard deviation?
Standard deviation is the variance from the mean of the data.
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.
How standard deviation and Mean deviation differ from each other?
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.
