Lines Matching refs:prob
872 prob = betai(0.5 * df, 0.5, df / float(df + t * t))
873 return r, prob
942 prob = betai(0.5 * df, 0.5, df / (df + t * t)) # t already a float
943 return rpb, prob
977 prob = erfcc(abs(z) / 1.4142136)
978 return tau, prob
1003 prob = betai(0.5 * df, 0.5, df / (df + t * t))
1007 return slope, intercept, r, prob, sterrest
1030 prob = betai(0.5 * df, 0.5, float(df) / (df + t * t))
1035 name, n, x, v, min(a), max(a), statname, t, prob)
1036 return t, prob
1061 prob = betai(0.5 * df, 0.5, df / (df + t * t))
1066 name2, n2, x2, v2, min(b), max(b), statname, t, prob)
1067 return t, prob
1100 prob = betai(0.5 * df, 0.5, df / (df + t * t))
1105 name2, n, x2, v2, min(b), max(b), statname, t, prob)
1106 return t, prob
1160 prob = ksprob((en + 0.12 + 0.11 / en) * abs(d))
1162 prob = 1.0
1163 return d, prob
1239 prob = 2 * (1.0 - zprob(abs(z)))
1240 return z, prob
1272 prob = 2 * (1.0 - zprob(abs(z)))
1273 return wt, prob
1454 prob = ((x + 1.0) * 0.5)
1456 prob = ((1.0 - x) * 0.5)
1457 return prob
1610 prob = fprob(dfbn, dfwn, f)
1611 return f, prob
1823 n2, m2, se2, min2, max2, statname, stat, prob): argument
1837 x = prob.shape
1838 prob = prob[0]
1841 if prob < 0.001:
1843 elif prob < 0.01:
1845 elif prob < 0.05:
1860 if prob.shape == ():
1861 prob = prob[0]
1864 print 'Test statistic = ', round(stat, 3), ' p = ', round(prob, 3), suffix
1875 if prob.shape == ():
1876 prob = prob[0]
1880 ' p = ', round(prob, 4), suffix, '\n\n']))
3118 prob = abetai(0.5 * betwdf, 0.5, betwdf / (betwdf + t * t), verbose)
3119 return rho, prob
3154 prob = abetai(0.5 * df, 0.5, df / (df + t * t), verbose)
3155 return r, prob
3205 prob = abetai(0.5 * df, 0.5, df / (df + t * t))
3206 return rpb, prob
3239 prob = erfcc(abs(z) / 1.4142136)
3240 return tau, prob
3273 prob = abetai(0.5 * df, 0.5, df / (df + t * t))
3277 return slope, intercept, r, prob, sterrest, n
3317 prob = abetai(0.5 * df, 0.5, df / (df + t * t))
3324 return slope, intercept, r, prob, sterrest, n
3348 prob = abetai(0.5 * df, 0.5, df / (df + t * t))
3354 N.maximum.reduce(N.ravel(a)), statname, t, prob)
3355 return t, prob
3430 prob = abetai(0.5 * df, 0.5, float(df) / (df + t * t))
3434 t = N.where(pval < prob, t + step, t - step)
3435 prob = abetai(0.5 * df, 0.5, float(df) / (df + t * t))
3552 prob = aksprob((en + 0.12 + 0.11 / en) * N.fabs(d))
3555 return d, prob
3627 prob = 2 * (1.0 - azprob(abs(z)))
3628 return z, prob
3657 prob = 2 * (1.0 - zprob(abs(z)))
3658 return wt, prob
3858 prob = N.where(N.greater(z, 0), (x + 1) * 0.5, (1 - x) * 0.5)
3859 return prob
4102 prob = fprob(dfbn, dfwn, f)
4103 return f, prob
4115 def outputfstats(Enum, Eden, dfnum, dfden, f, prob): argument
4121 prob = round(prob, 3)
4123 if prob < 0.001:
4125 elif prob < 0.01:
4127 elif prob < 0.05:
4130 lofl = title + [[Enum, dfnum, round(Enum / float(dfnum), 3), f, prob, suffix