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to calculate the standard deviation you must put each number in order from the least to the gr east then you must find your mean after you find your mean you must subtract your mean from each of the data set numbers once you finishsubtracting the data set numbers you add them up and divide by the amount of numbers there are and you have found the standard deviation.

Q: Give an example of how standard deviation can be useful Also why is underestimating the standard deviation as in the case with the Range Rule Thumb a better method than overestimating?

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It shows primarily that the measurement unit used for recording the data is very large. For example, the standard deviation of the heights of individuals, when recorded in metres, will be one hundredth of the standard deviation of their heights when recorded in centimetres. The process is known as coding.

Standard deviation can only be zero if all the data points in your set are equal. If all data points are equal, there is no deviation. For example, if all the participants in a survey coincidentally were all 30 years old, then the value of age would be 30 with no deviation. Thus, there would also be no standard deviation.A data set of one point (small sample) will always have a standard deviation of zero, because the one value doesn't deviate from itself at all.!

examples of deviation in poetry

A worked out example is shown in the related link. There are a number of calculators that do this automatically. Also, the Excel program (and most other spreadsheet programs) include a standard deviation function. In Excel, it is +stdev(a1:a10) for a list of numbers from a1 to a10.

It allows you to understand, or comprehend the average fluctuation to the average. example: the average height for adult men in the United States is about 70", with a standard deviation of around 3". This means that most men (about 68%, assuming a normal distribution) have a height within 3" of the mean (67"- 73"), one standard deviation, and almost all men (about 95%) have a height within 6" of the mean (64"-76"), two standard deviations. In summation standard deviation allows us to see the 'average' as a whole.

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Look at the Wikipedia article on "Standard deviation" - it includes an example right at the beginning.

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.

Yes. If the variance is less than 1, the standard deviation will be greater that the variance. For example, if the variance is 0.5, the standard deviation is sqrt(0.5) or 0.707.

It shows primarily that the measurement unit used for recording the data is very large. For example, the standard deviation of the heights of individuals, when recorded in metres, will be one hundredth of the standard deviation of their heights when recorded in centimetres. The process is known as coding.

A standard deviation is a statistical measure of the variation there in a population or group. A standard deviation of 1 means that 68% of the members of the population are withing plus or minus the value of the standard deviation from the average. For example: assume the average height of men is 5 feet 9 inches, and the standard deviation is three inches. Then 68% of all men are between 5' 6" and 6' which is 5'9" plus or minus 3 inches. [Note: this is only to illustrate and is not intended to be a real/correct statistic of men's heights.]

Standard deviation can only be zero if all the data points in your set are equal. If all data points are equal, there is no deviation. For example, if all the participants in a survey coincidentally were all 30 years old, then the value of age would be 30 with no deviation. Thus, there would also be no standard deviation.A data set of one point (small sample) will always have a standard deviation of zero, because the one value doesn't deviate from itself at all.!

Your middle point or line for the plot (mean) would be 6.375. Then you would add/subtract 1.47 from your mean. For example, one standard deviation would equal 6.375 + 1.47 and one standard deviation from the left would be 6.375 - 1.47

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.

You can calculate standard deviation by addin the numbers of data that are together and dividing that number by the amount pieces of data.THAT IS TOTALLY INCORRECT.What was answered above was the calculation for getting an (mean) average.If you take five numbers for example 1, 2, 3, 4, 5 then the (mean) average is 3.But the standard deviation between them is 1.58814 and the variance is 2.5Also the population std. deviation will be 1.41421 and the population variance will be 2.see standard-deviation.appspot.com/

the answer is overestimating and underestimating. An example is you could overestimate the money needed when going to the food store to shop for s party. An example for underestimating would be you could underestimate the number of guest that will attend the party. So you won't have a lot of left over food. If 100 people are invited most likely several people will not show up. I think over estimating is more useful, you never want to be caught without enough of anything. Extra is always best.

examples of deviation in poetry

The mean is the average of the numbers in your results. For example if your results are 7, 3 and 14, then your mean is 8. Numerically, (7+3+14)/3 The standard deviation measures how widely spread the values in a data set are.