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It is a measure of the spread of the results around their expected value.

It is a measure of the spread of the results around their expected value.

It is a measure of the spread of the results around their expected value.

It is a measure of the spread of the results around their expected value.

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It is a measure of the spread of the results around their expected value.

Q: Why is calculating the standard deviation an important part of data analysis?

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Collecting the data might be a good start.

The mean is 12 and each observation is 8 units away from 12.

They are sometimes used.

The standard deviation is the standard deviation! Its calculation requires no assumption.

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b-a/6

Assuming var is variance, simply square the standard deviation and the result is the variance.

Collecting the data might be a good start.

The mean is 12 and each observation is 8 units away from 12.

Standard deviation is a calculation. It I used in statistical analysis of a group of data to determine the deviation (the difference) between one datum point and the average of the group.For instance, on Stanford-Binet IQ tests, the average (or, mean) score is 100, and the standard deviation is 15. 65% of people will be within a standard deviation of the mean and score between 85 and 115 (100-15 and 100+15), while 95% of people will be within 2 standard deviations (30 points) of the mean -- between 70 and 130.

The goal is to disregard the influence of sample size. When calculating Cohen's d, we use the standard deviation in teh denominator, not the standard error.

No it is not correct.

They are sometimes used.

In terms of stock analysis, volatility.

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.

The Standard Deviation will give you an idea of how 'spread apart' the data is. Suppose the average gasoline prices in your town are 2.75 per gallon. A low standard deviation means many of the gas stations will have prices close to that price, while a high standard deviation means you would find prices much higher and also much lower than that average price.

Standard deviation helps planners and administrators to arrive at a figure that could be used to determine a range that can effectively describe a given set of numerical information/data; and based on which a decision concerning a system of those data can be made.