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GDB Spring 2014 | Dated: Aug 07, 14 |
Topic for Graded Discussion Board
“Since variance and standard deviation both are used to check variability in the data set. Discuss which one is more reliable to apply. Justify your answer at least with two appropriate examples."
Opening Date of Graded Discussion Board: Monday, August 11, 2014 at 12:01 a.m. Closing Date of Graded Discussion Board: Tuesday, August 12, 2014 at 11:59 p.m.
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They can be factors used to determine if there is a statistically important difference between mean values (the standard deviation is used in the computation of a confidence interval) such as Student's t test. Or if variances can be assumed or not assumed to be equal in doing Student's t test: you can evaluate this assumption by using the F test, where you compare variances of samples. So the values come in handy.
The main advantage of the standard deviation is that it is in the same units as the thing you are measuring.. For example, if you are measuring a length in cm, the standard deviation would be expressed in cm but the variance would be in (cm)^2
Standard deviation and variance are statistical measures of dispersion used to assess how far away individual data points are from the mean, or average, within a set of data. Knowing how far each data point lies from the average allows you to make more accurate conclusions about a data set, population or sample, because you can pick out outliers and extreme values and note how they affect your results.
Variance is defined as the mean of the squares of the individual differences from the mean. To calculate the variance within a set of data, you would first calculate the mean by adding up all of your data points and then dividing the total by the number of data points being used. Then, the mean is subtracted from each data point individually, the result is squared, and the mean is taken again from these values.
The standard deviation is calculated simply by taking the square root of the value that you get when you calculate the variance. Programs like Microsoft Excel and SPSS calculate both the variance and the standard deviation for you, and are very useful when working with large data sets as they tend to be more accurate than calculating the standard deviation by hand.
The variance of a data set measures the mathematical dispersion of the data relative to the mean. However, though this value is theoretically correct, it is difficult to apply in a real-world sense because the values used to calculate it were squared. The standard deviation, as the square root of the variance gives a value that is in the same units as the original values, which makes it much easier to work with and easier to interpret in conjunction with the concept of the normal curve
The variance of a data set measures the mathematical dispersion of the data relative to the mean. However, though this value is theoretically correct, it is difficult to apply in a real-world sense because the values used to calculate it were squared. The standard deviation, as the square root of the variance gives a value that is in the same units as the original values, which makes it much easier to work with and easier to interpret in conjunction with the concept of the normal curve.
Standard deviation
plzz simply Discuss which one is more reliable to apply. variance or standard deviation?
I think standard deviation is more reliable to apply,am i ri8?
yes u r right standard deviation is more reliable to apply,
Standard deviation and variance are statistical measures of dispersion used to assess how far away individual data points are from the mean, or average, within a set of data. Knowing how far each data point lies from the average allows you to make more accurate conclusions about a data set, population or sample, because you can pick out outliers and extreme values and note how they affect your results.
Variance is defined as the mean of the squares of the individual differences from the mean. To calculate the variance within a set of data, you would first calculate the mean by adding up all of your data points and then dividing the total by the number of data points being used. Then, the mean is subtracted from each data point individually, the result is squared, and the mean is taken again from these values.
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