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Welch's t-test: Difference between revisions

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Welch's t-test (view source)

Revision as of 03:29, 17 October 2019

932 bytes removed ,  4 years ago
m
→‎Using Burkardt's betain: cleaned up testing
m (→‎{{header|Perl}}: cleaned up testing)
m (→‎Using Burkardt's betain: cleaned up testing)
Line 1,211:
This uses the Soper reduction formula to evaluate the integral, which converges much more quickly than Simpson's formula.
 
<lang perl6>sub lgamma ( Num(Real) \n --> Num ){
sub lgamma ( Num(Real) \n --> Num ){
use NativeCall;
sub lgamma (num64 --> num64) is native {}
Line 1,299 ⟶ 1,298:
return $value;
}
my @d1 = 27.5,21.0,19.0,23.6,17.0,17.9,16.9,20.1,21.9,22.6,23.1,19.6,19.0,21.7,21.4;▼
my @d2 = 27.1,22.0,20.8,23.4,23.4,23.5,25.8,22.0,24.8,20.2,21.9,22.1,22.9,20.5,24.4;▼
my @d3 = 17.2,20.9,22.6,18.1,21.7,21.4,23.5,24.2,14.7,21.8;▼
my @d4 = 21.5,22.8,21.0,23.0,21.6,23.6,22.5,20.7,23.4,21.8,20.7,21.7,21.5,22.5,23.6,21.5,22.5,23.5,21.5,21.8;▼
my @d5 = 19.8,20.4,19.6,17.8,18.5,18.9,18.3,18.9,19.5,22.0;▼
my @d6 = 28.2,26.6,20.1,23.3,25.2,22.1,17.7,27.6,20.6,13.7,23.2,17.5,20.6,18.0,23.9,21.6,24.3,20.4,24.0,13.2;▼
my @d7 = 30.02,29.99,30.11,29.97,30.01,29.99;▼
my @d8 = 29.89,29.93,29.72,29.98,30.02,29.98;▼
my @x = 3.0,4.0,1.0,2.1;▼
my @y = 490.2,340.0,433.9;▼
my @s1 = 1.0/15,10.0/62.0;▼
my @s2 = 1.0/10,2/50.0;▼
my @v1 = 0.010268,0.000167,0.000167;▼
my @v2 = 0.159258,0.136278,0.122389;▼
my @z1 = 9/23.0,21/45.0,0/38.0;▼
my @z2 = 0/44.0,42/94.0,0/22.0;▼
 
my $error = 0;
 
my @answers = (
my @CORRECT_ANSWERS = (0.021378001462867,▼
0.021378001462867,
0.148841696605327,
0.0359722710297968,
Line 1,324 ⟶ 1,308:
0.00339907162713746,
0.52726574965384,
0.545266866977794);,
);
 
for (
my $pvalue = pvalue(@d1, @d2);▼
▲my @d1 = [< 27.5, 21.0, 19.0, 23.6, 17.0, 17.9, 16.9, 20.1, 21.9, 22.6, 23.1, 19.6, 19.0, 21.7, 21.4;>],
my $error = abs($pvalue - @CORRECT_ANSWERS[0]);▼
▲my @d2 = [< 27.1, 22.0, 20.8, 23.4, 23.4, 23.5, 25.8, 22.0, 24.8, 20.2, 21.9, 22.1, 22.9, 20.5, 24.4;>],
printf("Test sets 1 p-value = %.14g\n",$pvalue);▼
 
▲my @d3 = [< 17.2, 20.9, 22.6, 18.1, 21.7, 21.4, 23.5, 24.2, 14.7, 21.8;>],
$pvalue = pvalue(@d3, @d4);
▲my @d4 = [< 21.5, 22.8, 21.0, 23.0, 21.6, 23.6, 22.5, 20.7, 23.4, 21.8, 20.7, 21.7, 21.5, 22.5, 23.6, 21.5, 22.5, 23.5, 21.5, 21.8;>],
$error += abs($pvalue - @CORRECT_ANSWERS[1]);
printf("Test sets 2 p-value = %.14g\n",$pvalue);
 
▲my @d5 = [< 19.8, 20.4, 19.6, 17.8, 18.5, 18.9, 18.3, 18.9, 19.5, 22.0;>],
$pvalue = pvalue(@d5, @d6);
▲my @d6 = [< 28.2, 26.6, 20.1, 23.3, 25.2, 22.1, 17.7, 27.6, 20.6, 13.7, 23.2, 17.5, 20.6, 18.0, 23.9, 21.6, 24.3, 20.4, 24.0, 13.2;>],
$error += abs($pvalue - @CORRECT_ANSWERS[2]);
printf("Test sets 3 p-value = %.14g\n",$pvalue);
 
▲my @d7 = [< 30.02, 29.99, 30.11, 29.97, 30.01, 29.99;>],
$pvalue = pvalue(@d7, @d8);
▲my @d8 = [< 29.89, 29.93, 29.72, 29.98, 30.02, 29.98;>],
$error += abs($pvalue - @CORRECT_ANSWERS[3]);
printf("Test sets 4 p-value = %.14g\n",$pvalue);
 
▲my @x = [< 3.0, 4.0, 1.0, 2.1;>],
$pvalue = pvalue(@x, @y);
▲my @y = [< 490.2, 340.0, 433.9;>],
$error += abs($pvalue - @CORRECT_ANSWERS[4]);
printf("Test sets 5 p-value = %.14g\n",$pvalue);
 
▲my @v1 = [< 0.010268, 0.000167, 0.000167;>],
$pvalue = pvalue(@v1, @v2);
▲my @v2 = [< 0.159258, 0.136278, 0.122389;>],
$error += abs($pvalue - @CORRECT_ANSWERS[5]);
printf("Test sets 6 p-value = %.14g\n",$pvalue);
 
▲my @s1 = [< 1.0/15, 10.0/62.0;>],
$pvalue = pvalue(@s1, @s2);
▲my @s2 = [< 1.0/10, 2/50.0;>],
$error += abs($pvalue - @CORRECT_ANSWERS[6]);
printf("Test sets 7 p-value = %.14g\n",$pvalue);
 
$pvalue = pvalue(@z1, @z2);
$error += abs($pvalue - @CORRECT_ANSWERS[7]);
printf("Test sets 8 p-value = %.14g\n",$pvalue);
 
printf("the cumulative error is %g\n", $error);▼
 
</lang>
 
▲my @z1 = [< 9/23.0, 21/45.0, 0/38.0;>],
▲my @z2 = [< 0/44.0, 42/94.0, 0/22.0;>],
) -> @left, @right {
▲ my $pvalue = pvalue(p-value @d1left, @d2)right;
▲printf("Test sets 1 printf("p-value = %.14g\n",$pvalue);
▲my $error += abs($pvalue - shift @CORRECT_ANSWERS[0]answers);
}
▲printf("the cumulative error is %g\n", $error);</lang>
{{out}}
▲my @CORRECT_ANSWERS<pre>p-value = (0.021378001462867,
<pre>
Test sets 1 p-value = 0.02137800146286714884169660533
Test sets 2 p-value = 0.14884169660533035972271029797
Test sets 3 p-value = 0.035972271029797090773324285667
Test sets 4 p-value = 0.090773324285667010751561149784
Test sets 5 p-value = 0.0107515611497840033990716271375
Test sets 6 p-value = 0.003399071627137552726574965384
Test sets 7 p-value = 0.5272657496538454526686697779
the cumulative error is 5.50254e30131e-15</pre>▼
Test sets 8 p-value = 0.54526686697779
▲the cumulative error is 5.50254e-15
</pre>
 
=={{header|Phix}}==
SqrtNegInf
2,392

edits

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