#include "lexer.h"
#include "error.h"
#include "magic.h"
+#include "misc.h"
#include "tab.h"
#include "som.h"
#include "value-labels.h"
#include "var.h"
#include "vfm.h"
#include "hash.h"
-#include "stats.h"
#include "t-test.h"
#include "levene.h"
df = gs0->n + gs1->n - 2.0 ;
tab_float (self->t, 5, i*2+3, TAB_RIGHT, df, 2, 0);
- pooled_variance = ( (gs0->n )*sqr(gs0->s_std_dev)
+ pooled_variance = ( (gs0->n )*pow2(gs0->s_std_dev)
+
- (gs1->n )*sqr(gs1->s_std_dev)
+ (gs1->n )*pow2(gs1->s_std_dev)
) / df ;
t = (gs0->mean - gs1->mean) / sqrt(pooled_variance) ;
tab_float(self->t, 7, i*2+3, TAB_RIGHT, mean_diff, 8, 3);
- std_err_diff = sqrt( sqr(gs0->se_mean) + sqr(gs1->se_mean));
+ std_err_diff = sqrt( pow2(gs0->se_mean) + pow2(gs1->se_mean));
tab_float(self->t, 8, i*2+3, TAB_RIGHT, std_err_diff, 8, 3);
TAB_LEFT, _("Equal variances not assumed"));
- se2 = (sqr(gs0->s_std_dev)/(gs0->n -1) ) +
- (sqr(gs1->s_std_dev)/(gs1->n -1) );
+ se2 = (pow2(gs0->s_std_dev)/(gs0->n -1) ) +
+ (pow2(gs1->s_std_dev)/(gs1->n -1) );
t = mean_diff / sqrt(se2) ;
tab_float (self->t, 4, i*2+3+1, TAB_RIGHT, t, 8, 3);
- df = sqr(se2) / (
- (sqr(sqr(gs0->s_std_dev)/(gs0->n - 1 ))
+ df = pow2(se2) / (
+ (pow2(pow2(gs0->s_std_dev)/(gs0->n - 1 ))
/(gs0->n -1 )
)
+
- (sqr(sqr(gs1->s_std_dev)/(gs1->n - 1 ))
+ (pow2(pow2(gs1->s_std_dev)/(gs1->n - 1 ))
/(gs1->n -1 )
)
) ;
t = (pairs[i].mean[0] - pairs[i].mean[1])
/ sqrt (
- ( sqr (pairs[i].s_std_dev[0]) + sqr (pairs[i].s_std_dev[1]) -
+ ( pow2 (pairs[i].s_std_dev[0]) + pow2 (pairs[i].s_std_dev[1]) -
2 * pairs[i].correlation *
pairs[i].s_std_dev[0] * pairs[i].s_std_dev[1] )
/ (n - 1)
double correlation_t =
pairs[i].correlation * sqrt(df) /
- sqrt(1 - sqr(pairs[i].correlation));
+ sqrt(1 - pow2(pairs[i].correlation));
/* row headings */
pairs[i].sum[0] += weight * val0->f;
pairs[i].sum[1] += weight * val1->f;
- pairs[i].ssq[0] += weight * sqr(val0->f);
- pairs[i].ssq[1] += weight * sqr(val1->f);
+ pairs[i].ssq[0] += weight * pow2(val0->f);
+ pairs[i].ssq[1] += weight * pow2(val1->f);
pairs[i].sum_of_prod += weight * val0->f * val1->f ;
pairs[i].sum_of_diffs += weight * ( val0->f - val1->f ) ;
- pairs[i].ssq_diffs += weight * sqr(val0->f - val1->f);
+ pairs[i].ssq_diffs += weight * pow2(val0->f - val1->f);
}
}
{
pairs[i].mean[j] = pairs[i].sum[j] / n ;
pairs[i].s_std_dev[j] = sqrt((pairs[i].ssq[j] / n -
- sqr(pairs[i].mean[j]))
+ pow2(pairs[i].mean[j]))
);
pairs[i].std_dev[j] = sqrt(n/(n-1)*(pairs[i].ssq[j] / n -
- sqr(pairs[i].mean[j]))
+ pow2(pairs[i].mean[j]))
);
}
pairs[i].std_dev_diff = sqrt ( n / (n - 1) * (
( pairs[i].ssq_diffs / n )
-
- sqr(pairs[i].mean_diff )
+ pow2(pairs[i].mean_diff )
) );
}
}
{
gs->n+=weight;
gs->sum+=weight * val->f;
- gs->ssq+=weight * sqr(val->f);
+ gs->ssq+=weight * pow2(val->f);
}
}