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In probability theory and statistics, variance is a measure of dispersion, meaning it is a measure of how far a set of numbers are spread out from their average value. It is defined as the expected value of the squared deviation from the mean of a random variable. The standard deviation is the square root of the variance. Technically, it is the second…
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displaystyle var operatorname sample sum left right mean random population sigma distribution variables variable frac observations covariance value deviation mu
| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| Variance | is a | measure of dispersion | 0.90 | text |
| Variance | is a | characteristic of a set of observations | 0.90 | text |
| Variance | is a | U-statistic for the function f | 0.90 | text |
| Variance | is a | population variance 932.743 as the sum of the squared deviations about the mean of this set | 0.90 | text |
| Variance | is a | real scalar.For vector-valued random variablesAs a matrixIf X | 0.90 | text |
| Variance | is a | real scalar | 0.90 | text |
| the expected absolute deviation | instance of | An advantage of variance as a measure of dispersion is that it is more amenable to algebraic manipulation than other measures of dispersion | 0.80 | text |
| the measurements of yesterday's rain throughout the day typically cannot be complete sets of all possible observations that could be made | instance of | Population variance and sample varianceReal-world observations | 0.80 | text |
| Variance | measured by | Unlike | 0.60 | section |
| Variance | measured by | For | 0.60 | section |
| Variance | measured by | In | 0.60 | section |
| Variance | measured by | The | 0.60 | section |
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