Scippy

SCIP

Solving Constraint Integer Programs

Methods for statistical tests

Detailed Description

Functions

SCIP_Real SCIPstudentTGetCriticalValue (SCIP_CONFIDENCELEVEL clevel, int df)
 
SCIP_Real SCIPcomputeTwoSampleTTestValue (SCIP_Real meanx, SCIP_Real meany, SCIP_Real variancex, SCIP_Real variancey, SCIP_Real countx, SCIP_Real county)
 
SCIP_Real SCIPerf (SCIP_Real x)
 
SCIP_Real SCIPnormalGetCriticalValue (SCIP_CONFIDENCELEVEL clevel)
 
SCIP_Real SCIPnormalCDF (SCIP_Real mean, SCIP_Real variance, SCIP_Real value)
 

Function Documentation

SCIP_Real SCIPstudentTGetCriticalValue ( SCIP_CONFIDENCELEVEL  clevel,
int  df 
)

get critical value of a Student-T distribution for a given number of degrees of freedom at a confidence level

Parameters
clevel(one-sided) confidence level
dfdegrees of freedom
SCIP_Real SCIPcomputeTwoSampleTTestValue ( SCIP_Real  meanx,
SCIP_Real  meany,
SCIP_Real  variancex,
SCIP_Real  variancey,
SCIP_Real  countx,
SCIP_Real  county 
)

compute a t-value for the hypothesis that x and y are from the same population; Assuming that x and y represent normally distributed random samples with equal variance, the returned value comes from a Student-T distribution with countx + county - 2 degrees of freedom; this value can be compared with a critical value (see also SCIPstudentTGetCriticalValue()) at a predefined confidence level for checking if x and y significantly differ in location

Parameters
meanxthe mean of the first distribution
meanythe mean of the second distribution
variancexthe variance of the x-distribution
varianceythe variance of the y-distribution
countxnumber of samples of x
countynumber of samples of y
SCIP_Real SCIPerf ( SCIP_Real  x)

returns the value of the Gauss error function evaluated at a given point

Parameters
xvalue to evaluate
SCIP_Real SCIPnormalGetCriticalValue ( SCIP_CONFIDENCELEVEL  clevel)

get critical value of a standard normal distribution at a given confidence level

Parameters
clevel(one-sided) confidence level
SCIP_Real SCIPnormalCDF ( SCIP_Real  mean,
SCIP_Real  variance,
SCIP_Real  value 
)

calculates the cumulative distribution P(-infinity <= x <= value) that a normally distributed random variable x takes a value between -infinity and parameter value.

The distribution is given by the respective mean and deviation. This implementation uses the error function erf().

Parameters
meanthe mean value of the distribution
variancethe square of the deviation of the distribution
valuethe upper limit of the calculated distribution integral