2.9 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≤p ≤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≤p ≤1 $.$

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

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

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

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

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

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

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

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

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

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

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

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

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

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

2.9 The stats module

2.9 The `stats` module