Q: How does one handle data decompression on a standard PC?

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The Empirical Rule states that 68% of the data falls within 1 standard deviation from the mean. Since 1000 data values are given, take .68*1000 and you have 680 values are within 1 standard deviation from the mean.

One can just convert it themselves. For example 1 inch is 2.54cm, and one pound is .45kg. There are many conversion tables that can be found, both in print and online that give the full list of standard to metric conversions. Also many scientific calculators have the conversions built in so one just has to enter data and it converts it for them.

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.!

Some formulas statisticians may use are population mean, sample mean, variance, and standard deviation. They may also use linear regression line and standard error equations. Another can be the mean value of a data set, where one adds all data points given in a set and divides this number by the number of data points in the set.

A standard distribution regards 95% of all data being within 2-standard deviations of either side. Similarly, within one standard deviation either way is 68% of all data. This creates a bell curve distribution. An abnormal distribution would be erratic and not follow such a statistical structure of representation.

Related questions

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.

No

One standard deviation for one side will be 34% of data. So within 1 std. dev. to both sides will be 68% (approximately) .the data falls outside 1 standard deviation of the mean will be 1.00 - 0.68 = 0.32 (32 %)

The data point is close to the expected value.

Yes, it does. If the data are sample data, than the divisor is N. If the data are the entire population, than the divisor is N-1 is account for the loss of one degree of freedom in the calculation of both the mean and the standard deviation from the same data.

The 68-95-99.7 rule states that in a normally distributed set of data, approximately 68% of all observations lie within one standard deviation either side of the mean, 95% lie within two standard deviations and 99.7% lie within three standard deviations.Or looking at it cumulatively:0.15% of the data lie below the mean minus three standard deviations2.5% of the data lie below the mean minus two standard deviations16% of the data lie below the mean minus one standard deviation50 % of the data lie below the mean84 % of the data lie below the mean plus one standard deviation97.5% of the data lie below the mean plus two standard deviations99.85% of the data lie below the mean plus three standard deviationsA normally distributed set of data with mean 100 and standard deviation of 20 means that a score of 140 lies two standard deviations above the mean. Hence approximately 97.5% of all observations are less than 140.

It's used in determining how far from the standard (average) a certain item or data point happen to be. (Ie, one standard deviation; two standard deviations, etc.)

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.

The Empirical Rule states that 68% of the data falls within 1 standard deviation from the mean. Since 1000 data values are given, take .68*1000 and you have 680 values are within 1 standard deviation from the mean.

They are measures of the spread of the data and constitute one of the key descriptive statistics.

One can just convert it themselves. For example 1 inch is 2.54cm, and one pound is .45kg. There are many conversion tables that can be found, both in print and online that give the full list of standard to metric conversions. Also many scientific calculators have the conversions built in so one just has to enter data and it converts it for them.

Anand Mehta said yes and this is correct. You will get a SD, for example, if all of the data points are less than one, or if the data points are very close together and there is not much spread in the data..