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In statistics, the Q-function is the tail distribution function of the standard normal distribution. In other words, Q ( x ) {\displaystyle Q(x)} is the probability that a normal (Gaussian) random variable will obtain a value larger than x {\displaystyle x} standard deviations. Equivalently, Q ( x ) {\displaystyle Q(x)} is the probability that a standard…
Standards, Bounds and approximations & Inverse Q
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normal displaystyle function standard distribution gaussian random variable form error value larger cumulative also positive simple expressed terms probability bounds
| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| Q-function | is a | tail distribution function of the standard normal distribution | 0.90 | text |
| a good trade-off between accuracy | instance of | This approximation offers some benefits | 0.80 | text |
| analytical tractability | instance of | This approximation offers some benefits | 0.80 | text |
| R | instance of | ValuesThe Q-function is well tabulated and can be computed directly in most of the mathematical software packages | 0.80 | text |
| those available in Python | instance of | ValuesThe Q-function is well tabulated and can be computed directly in most of the mathematical software packages | 0.80 | text |
| MATLAB | instance of | ValuesThe Q-function is well tabulated and can be computed directly in most of the mathematical software packages | 0.80 | text |
| Mathematica | instance of | ValuesThe Q-function is well tabulated and can be computed directly in most of the mathematical software packages | 0.80 | text |
| Q-function | related to Bounds and approximations | The Q-function | 0.60 | section |
| Q-function | related to Bounds and approximations | However | 0.60 | section |
| Q-function | related to Bounds and approximations | Tighter | 0.60 | section |
| Q-function | related to Definition and basic properties | Formally | 0.60 | section |
| Q-function | related to Definition and basic properties | Thus | 0.60 | section |
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