Table 16: Statistical analysis for frontier comparisons. Observed DMUs are evaluated against the 3 different frontiers to determine their efficiency scores which are calculated using the normal DEA model and to determine if the efficiency scores for each group are substantially different when comparing EFF to DEA, EFF to DCF, and EFF to CCP.

Exp.grp+
EFF
Exp.grp+
DEA
Exp.grp+
EFF
Exp.grp+
DCF
Exp.grp+
EFF
Exp.grp+
CCP

Simulation 1
 Mean
 Variance
 Observations
 Pearson correlation
 Hypothesized mean difference
 Df
Rank-sum test1.28580.91250.9125
stat
P (T ≤ t)   two tail0.00140.042350.03372
critical two tail

Simulation 2
 Mean
 Variance
 Observations
 Pearson correlation
 Hypothesized mean difference
 Df
Rank-sum test1.30660.97471.0162
stat
P (T ≤ t)   two tail0.000370.0310.02611
critical two tail

Simulation 3
 Mean
 Variance
 Observations
 Pearson
correlation
 Hypothesized mean difference
 Df
Rank-sum test0.7259−0.0622−0.6014
stat
P (T ≤ t)   two tail0.09140.77290.1137
critical two tail

Simulation 4
 Mean
 Variance
 Observations
 Pearson correlation
 Hypothesized mean difference
Df141414
Rank-sum test0.85030.1452−0.394
stat
P (T ≤ t)   two tail0.07480.7599−0.312
critical two tail2.1452.1452.145