mirror of
				https://github.com/saitohirga/WSJT-X.git
				synced 2025-10-25 01:50:30 -04:00 
			
		
		
		
	
		
			
				
	
	
		
			127 lines
		
	
	
		
			4.5 KiB
		
	
	
	
		
			C++
		
	
	
	
	
	
			
		
		
	
	
			127 lines
		
	
	
		
			4.5 KiB
		
	
	
	
		
			C++
		
	
	
	
	
	
| // students_t_example2.cpp
 | |
| 
 | |
| // Copyright Paul A. Bristow 2006.
 | |
| // Use, modification and distribution are subject to the
 | |
| // Boost Software License, Version 1.0.
 | |
| // (See accompanying file LICENSE_1_0.txt
 | |
| // or copy at http://www.boost.org/LICENSE_1_0.txt)
 | |
| 
 | |
| // Example 2 of using Student's t
 | |
| 
 | |
| // A general guide to Student's t is at
 | |
| // http://en.wikipedia.org/wiki/Student's_t-test
 | |
| // (and many other elementary and advanced statistics texts).
 | |
| // It says:
 | |
| // The t statistic was invented by William Sealy Gosset
 | |
| // for cheaply monitoring the quality of beer brews.
 | |
| // "Student" was his pen name.
 | |
| // Gosset was statistician for Guinness brewery in Dublin, Ireland,
 | |
| // hired due to Claude Guinness's innovative policy of recruiting the
 | |
| // best graduates from Oxford and Cambridge for applying biochemistry
 | |
| // and statistics to Guinness's industrial processes.
 | |
| // Gosset published the t test in Biometrika in 1908,
 | |
| // but was forced to use a pen name by his employer who regarded the fact
 | |
| // that they were using statistics as a trade secret.
 | |
| // In fact, Gosset's identity was unknown not only to fellow statisticians
 | |
| // but to his employer - the company insisted on the pseudonym
 | |
| // so that it could turn a blind eye to the breach of its rules.
 | |
| 
 | |
| // The Students't distribution function is described at
 | |
| // http://en.wikipedia.org/wiki/Student%27s_t_distribution
 | |
| 
 | |
| #include <boost/math/distributions/students_t.hpp>
 | |
|    using boost::math::students_t;  // Probability of students_t(df, t).
 | |
| 
 | |
| #include <iostream>
 | |
|    using std::cout;
 | |
|    using std::endl;
 | |
| #include <iomanip>
 | |
|    using std::setprecision;
 | |
|    using std::setw;
 | |
| #include <cmath>
 | |
|    using std::sqrt;
 | |
| 
 | |
| // This example of a one-sided test is from:
 | |
| //
 | |
| // from Statistics for Analytical Chemistry, 3rd ed. (1994), pp 59-60
 | |
| // J. C. Miller and J. N. Miller, Ellis Horwood ISBN 0 13 0309907.
 | |
| 
 | |
| // An acid-base titrimetric method has a significant indicator error and
 | |
| // thus tends to give results with a positive systematic error (+bias).
 | |
| // To test this an exactly 0.1 M solution of acid is used to titrate
 | |
| // 25.00 ml of exactly 0.1 M solution of alkali,
 | |
| // with the following results (ml):
 | |
| 
 | |
| double reference = 25.00; // 'True' result.
 | |
| const int values = 6; // titrations.
 | |
| double data [values] = {25.06, 25.18, 24.87, 25.51, 25.34, 25.41};
 | |
| 
 | |
| int main()
 | |
| {
 | |
|    cout << "Example2 using Student's t function. ";
 | |
| #if defined(__FILE__) && defined(__TIMESTAMP__) && defined(_MSC_FULL_VER)
 | |
|    cout << "  " << __FILE__ << ' ' << __TIMESTAMP__ << ' '<< _MSC_FULL_VER;
 | |
| #endif
 | |
|    cout << endl;
 | |
| 
 | |
|    double sum = 0.;
 | |
|    for (int value = 0; value < values; value++)
 | |
|    { // Echo data and calculate mean.
 | |
|       sum += data[value];
 | |
|       cout << setw(4) << value << ' ' << setw(14) << data[value] << endl;
 | |
|    }
 | |
|    double mean = sum /static_cast<double>(values);
 | |
|    cout << "Mean = " << mean << endl; // 25.2283
 | |
| 
 | |
|    double sd = 0.;
 | |
|    for (int value = 0; value < values; value++)
 | |
|    { // Calculate standard deviation.
 | |
|       sd +=(data[value] - mean) * (data[value] - mean);
 | |
|    }
 | |
|    int degrees_of_freedom = values - 1; // Use the n-1 formula.
 | |
|    sd /= degrees_of_freedom; // == variance.
 | |
|    sd= sqrt(sd);
 | |
|    cout << "Standard deviation = " << sd<< endl; // = 0.238279
 | |
| 
 | |
|    double t = (mean - reference) * sqrt(static_cast<double>(values))/ sd; //
 | |
|    cout << "Student's t = " << t << ", with " << degrees_of_freedom << " degrees of freedom." << endl; // = 2.34725
 | |
| 
 | |
|    cout << "Probability of positive bias is " << cdf(students_t(degrees_of_freedom), t) << "."<< endl; // =  0.967108.
 | |
|    // A 1-sided test because only testing for a positive bias.
 | |
|    // If > 0.95 then greater than 1 in 20 conventional (arbitrary) requirement.
 | |
| 
 | |
|    return 0;
 | |
| }  // int main()
 | |
| 
 | |
| /*
 | |
| 
 | |
| Output is:
 | |
| 
 | |
| ------ Build started: Project: students_t_example2, Configuration: Debug Win32 ------
 | |
| Compiling...
 | |
| students_t_example2.cpp
 | |
| Linking...
 | |
| Autorun "i:\boost-06-05-03-1300\libs\math\test\Math_test\debug\students_t_example2.exe"
 | |
| Example2 using Student's t function.   ..\..\..\..\..\..\boost-sandbox\libs\math_functions\example\students_t_example2.cpp Sat Aug 12 16:55:59 2006 140050727
 | |
|    0          25.06
 | |
|    1          25.18
 | |
|    2          24.87
 | |
|    3          25.51
 | |
|    4          25.34
 | |
|    5          25.41
 | |
| Mean = 25.2283
 | |
| Standard deviation = 0.238279
 | |
| Student's t = 2.34725, with 5 degrees of freedom.
 | |
| Probability of positive bias is 0.967108.
 | |
| Build Time 0:03
 | |
| Build log was saved at "file://i:\boost-06-05-03-1300\libs\math\test\Math_test\students_t_example2\Debug\BuildLog.htm"
 | |
| students_t_example2 - 0 error(s), 0 warning(s)
 | |
| ========== Build: 1 succeeded, 0 failed, 0 up-to-date, 0 skipped ==========
 | |
| 
 | |
| */
 | |
| 
 | |
| 
 | |
| 
 | |
| 
 | |
| 
 |