Class for Student's t-test. More...

#include <yat/statistics/tTest.h>

Public Member Functions
	tTest (void)
	Default Constructor.
void	add (double value, bool target, double weight=1.0)
void	reset (void)
	Set everything to zero.
double	score (void)
double	p_value () const
double	p_value_one_sided (void) const

Detailed Description

Class for Student's t-test.

See http://en.wikipedia.org/wiki/Student's_t-test for more details on the t-test.

Member Function Documentation

void theplu::yat::statistics::tTest::add	(	double	value,
		bool	target,
		double	weight = `1.0`
	)

Adding a data value to tTest.

double theplu::yat::statistics::tTest::p_value ( ) const

Calculates the p-value, i.e. the probability of observing a t-score equally or larger if the null hypothesis is true. If P is near zero, this casts doubt on this hypothesis. The null hypothesis is that the means of the two distributions are equal. Assumtions for this test is that the two distributions are normal distributions with equal variance. The latter assumtion is dropped in Welch's t-test.

Returns:: the two-sided p-value

double theplu::yat::statistics::tTest::p_value_one_sided ( void ) const

Returns:: One-sided P-value

void theplu::yat::statistics::tTest::reset ( void )

Set everything to zero.

Since:: New in yat 0.5

double theplu::yat::statistics::tTest::score ( void )

  Calculates the t-score, i.e. the ratio between difference in
  mean and standard deviation of this difference. The t-score is
  calculated as

$t = \frac{ m_x - m_y }{ s\sqrt{\frac{1}{n_x}+\frac{1}{n_y}}}$ where $ m $ is the weighted mean, n is the weighted version of number of data points $\frac{\left(\sum w_i\right)^2}{\sum w_i^2}$ , and $ s^2 $ is an estimation of the variance $s^2 = \frac{ \sum_i w_i(x_i-m_x)^2 + \sum_i w_i(y_i-m_y)^2 }{ n_x + n_y - 2 }$

  \see AveragerWeighted

  If all weights are equal to unity this boils down to

$t = \frac{ m_x - m_y } {s\sqrt{\frac{1}{n_x}+\frac{1}{n_y}}}$ where $ m $ is the mean, $ n $ is the number of data points and $s^2 = \frac{ \sum_i (x_i-m_x)^2 + \sum_i (y_i-m_y)^2 }{ n_x + n_y - 2 }$

See also:: Averager

Returns:: t-score.

The documentation for this class was generated from the following file:

yat/statistics/tTest.h

Public Member Functions

Detailed Description

Member Function Documentation