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
df	degrees 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

meanx	the mean of the first distribution
meany	the mean of the second distribution
variancex	the variance of the x-distribution
variancey	the variance of the y-distribution
countx	number of samples of x
county	number of samples of y

SCIP_Real SCIPerf

(

SCIP_Real

x

)

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

Parameters

x	value 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

mean	the mean value of the distribution
variance	the square of the deviation of the distribution
value	the upper limit of the calculated distribution integral