More about Discovery Process Models
77
Reduction of Uncertainty
With both LDSCV and NDSCV methods, estimation uncertainty
decreases when sample size increases, as demonstrated by the following procedure.
A random sample of size
1.
N (= 300) was drawn from the superpopulation (µ = 0.0 and σ
2 = 5.0).
Figure 4.10. (A–D) Pool-size-by-rank plots for a Pareto population derived by
LDSCV when n = 30 (A) and n = 50 (B), and plots derived by NDSCV when
n = 30 (C) and n = 50 (D). Prediction interval is the 0.9 probability level.
77
Reduction of Uncertainty
With both LDSCV and NDSCV methods, estimation uncertainty
decreases when sample size increases, as demonstrated by the following procedure.
A random sample of size
1.
N (= 300) was drawn from the superpopulation (µ = 0.0 and σ
2 = 5.0).
Figure 4.10. (A–D) Pool-size-by-rank plots for a Pareto population derived by
LDSCV when n = 30 (A) and n = 50 (B), and plots derived by NDSCV when
n = 30 (C) and n = 50 (D). Prediction interval is the 0.9 probability level.