# r2_is n=6 nsamples=2000000 seed=12 imax=30; mean C = 1.66737 (2-2/n = 1.66667); walks absorbed before imax: 2000000
# columns: label i  r=E N_i e^{i/n}  SE  relSE  e^{-y^2/2 or -i^2/(2n^3)}  E N_i e^{i/n+i^2/(2n^3)}  heuristic (1-i/n^2)^n e^{i/n}
y=0.25 4 1.217421 6.21e-04 5.10e-04 0.969233 1.263356 0.960759
y=0.50 7 1.227288 7.18e-04 5.85e-04 0.882497 1.374696 0.877506
y=1.00 15 0.678450 1.18e-03 1.74e-03 0.606531 1.142122 0.479995
y=1.50 22 0.073939 2.69e-03 3.64e-02 0.324652 0.226697 0.135321
y=2.00 29 0.000000 0.00e+00 nan 0.135335 0.000000 0.006790
x=0.5 3 1.201963 5.83e-04 4.85e-04 0.979382 1.227266 0.978173
x=1.0 6 1.242551 6.86e-04 5.52e-04 0.920044 1.350533 0.910347
x=2.0 12 0.994331 1.00e-03 1.01e-03 0.716531 1.387701 0.648696
x=4.0 24 0.000000 0.00e+00 nan 0.263597 0.000000 0.074895
# sum_{i<=imax} E N_i = 5.000000 (n-1 = 5);  mean excursion length sum i E N_i/(n-1) = 6.0009 (n = 6; rel dev 1.49e-04)
# sum_{i>=1} |E N_i - e^{-i/n}| (i<=imax, plus e^{-i/n} tail 3.72e-02) = 1.9980  [upward-biased by MC noise]
