yat  0.11.3pre
Public Member Functions | List of all members
theplu::yat::statistics::tTest Class Reference

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) const
 
double p_left (void) const
 
double p_right (void) const
 
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_left ( void  ) const
Returns
the probability of observing a t-score that is equal or smaller than score().
double theplu::yat::statistics::tTest::p_right ( void  ) const
Returns
the one-sided p-value, i.e., the probability of observing a t-score that is equal or greater than observed here.
double theplu::yat::statistics::tTest::p_value ( ) const

Calculates the two-sided p-value, i.e., the probability to observe a t-score equal (or greater) than |t| or smaller than -|t|, where t is the observed t-score (returned by score()).

Returns
the two-sided p-value
double theplu::yat::statistics::tTest::p_value_one_sided ( void  ) const
Deprecated:
Provided for backward compatibility with 0.10 API. Use p_right() instead.
void theplu::yat::statistics::tTest::reset ( void  )

Set everything to zero.

Since
New in yat 0.5
double theplu::yat::statistics::tTest::score ( void  ) const

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 Also
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:

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