Pyxplot Users' Guide: The stats module

12.9 The `stats` module

The stats module contains statistical functions:

stats.binomialCDF( $k,p,n$ )
The stats.binomialCDF( $k,p,n$ ) function evaluates the probability of getting fewer than or exactly $k$ successes out of $n$ trials in a binomial distribution with success probability $p$ . $k$ and $n$ must be positive real integers. $p$ must be a real number in the range $0\leq p \leq 1$ .

stats.binomialPDF( $k,p,n$ )
The stats.binomialPDF( $k,p,n$ ) function evaluates the probability of getting $k$ successes out of $n$ trials in a binomial distribution with success probability $p$ . $k$ and $n$ must be positive real integers. $p$ must be a real number in the range $0\leq p \leq 1$ .

stats.chisqCDF( $x,\nu$ )
The stats.chisqCDF( $x,\nu$ ) function returns the cumulative probability density at $x$ in a $\chi$ -squared distribution with $\nu$ degrees of freedom. $\nu$ must be a positive real dimensionless integer. $x$ must be a positive real dimensionless number.

stats.chisqCDFi( $P,\nu$ )
The stats.chisqCDFi( $P,\nu$ ) function returns the point $x$ at which the cumulative probability density in a $\chi$ -squared distribution with $\nu$ degrees of freedom is $P$ . $\nu$ must be a positive real dimensionless integer. $P$ must be a real number in the range $0\leq p \leq 1$ .

stats.chisqPDF( $x,\nu$ )
The stats.chisqPDF( $x,\nu$ ) function returns the probability density at $x$ in a $\chi$ -squared distribution with $\nu$ degrees of freedom. $\nu$ must be a positive real dimensionless integer. $x$ must be a positive real dimensionless number.

stats.gaussianCDF( $x,\sigma$ )
The stats.gaussianCDF( $x,\sigma$ ) function evaluates the Gaussian cumulative distribution function of standard deviation $\sigma$ at $x$ . The distribution is centred upon $x=0$ . $x$ and $\sigma$ must both be real, but may have any physical dimensions so long as they match.

stats.gaussianCDFi( $x,\sigma$ )
The stats.gaussianCDFi( $x,\sigma$ ) function evaluates the inverse Gaussian cumulative distribution function of standard deviation $\sigma$ at $x$ . The distribution is centred upon $x=0$ . $x$ and $\sigma$ must both be real, but may have any physical dimensions so long as they match.

stats.gaussianPDF( $x,\sigma$ )
The stats.gaussianPDF( $x,\sigma$ ) function evaluates the Gaussian probability density function of standard deviation $\sigma$ at $x$ . The distribution is centred upon $x=0$ . $x$ and $\sigma$ must both be real, but may have any physical dimensions so long as they match.

stats.lognormalCDF( $x,\zeta ,\sigma$ )
The stats.lognormalCDF( $x,\zeta ,\sigma$ ) function evaluates the log normal cumulative distribution function of standard deviation $\sigma$ , centred upon $\zeta$ , at $x$ . $\sigma$ must be real, positive and dimensionless. $x$ and $\zeta$ must both be real, but may have any physical dimensions so long as they match.

stats.lognormalCDFi( $x,\zeta ,\sigma$ )
The stats.lognormalCDFi( $x,\zeta ,\sigma$ ) function evaluates the inverse log normal cumulative distribution function of standard deviation $\sigma$ , centred upon $\zeta$ , at $x$ . $\sigma$ must be real, positive and dimensionless. $x$ and $\zeta$ must both be real, but may have any physical dimensions so long as they match.

stats.lognormalPDF( $x,\zeta ,\sigma$ )
The stats.lognormalPDF( $x,\zeta ,\sigma$ ) function evaluates the log normal probability density function of standard deviation $\sigma$ , centred upon $\zeta$ , at $x$ . $\sigma$ must be real, positive and dimensionless. $x$ and $\zeta$ must both be real, but may have any physical dimensions so long as they match.

stats.poissonCDF( $x,\mu$ )
The stats.poissonCDF( $x,\mu$ ) function returns the probability of getting $\leq x$ from a Poisson distribution with mean $\mu$ , where $\mu$ must be real, positive and dimensionless and $x$ must be real and dimensionless.

stats.poissonPDF( $x,\mu$ )
The stats.poissonPDF( $x,\mu$ ) function returns the probability of getting $x$ from a Poisson distribution with mean $\mu$ , where $\mu$ must be real, positive and dimensionless and $x$ must be a real dimensionless integer.

stats.tdistCDF( $x,\nu$ )
The stats.tdistCDF( $x,\nu$ ) function returns the cumulative probability density at $x$ in a $t$ -distribution with $\nu$ degrees of freedom. $\nu$ must be a positive real dimensionless integer. $x$ must be a positive real dimensionless number.

stats.tdistCDFi( $P,\nu$ )
The stats.tdistCDFi( $P,\nu$ ) function returns the point $x$ at which the cumulative probability density in a $t$ -distribution with $\nu$ degrees of freedom is $P$ . $\nu$ must be a positive real dimensionless integer. $P$ must be a real number in the range $0\leq p \leq 1$ .

stats.tdistPDF( $x,\nu$ )
The stats.tdistPDF( $x,\nu$ ) function returns the probability density at $x$ in a $t$ -distribution with $\nu$ degrees of freedom. $\nu$ must be a positive real dimensionless integer. $x$ must be a positive real dimensionless number.

12.9 The stats module

12.9 The `stats` module