However, empirical studies of the cost impact of design errors on variation are scarce.
To account for the survey design of the CHMS, standard errors, coefficients of variation and 95% confidence intervals were estimated using the bootstrap technique and specifying 13 denominator degrees of freedom in the SUDAAN procedure statements [ 20].
The mean phenotypic data of three replicates in different trials from all 102 lines (include100 DH lines and their 2 parental lines) were analyzed for frequency distributions, standard errors, coefficient of variation and ANOVA using SPSS 10.0.
There are a number of factors that can contribute to errors of measurement: variation in the difficulty of questions from one exam to another, variation of examiners' leniency and training on assessment protocols, variation in the complexity of the patient encounters, or variation in the examination procedure itself (e.g., use of paper and pencil versus computer, time of day, location).
The distributions of prediction errors for the three algorithms (Fig. 3) showed that ARSER and COSOPT were more accurate (errors with zero means and small variations) in predicting the period length compared with Fisher's G-test (errors of large variation).
Thus, the mercury effect was significant (5% level) when the PCB indicator was assumed to be error free, but it became insignificant assuming that the error coefficient of variation in the PCB measurement was 46%.
The errors of angular variation kNθ have been well compensated.
The errors of angular variation kNθ can be represented based on Zernike polynomials and additional shear rotation measurement [9].
a Before compensation, PV=8.27nm, RMS=0.80nm b After compensation, PV=6.30nm, RMS=0.70nm Fig. 3 Compensated errors of angular variation kNθ. a N=6, PV=8.22nm, RMS=1.07nm b N=12, PV=6.30nm, RMS=0.81nm.
The differences of Fig. 1a and b after W kNθ compensation can be seen from Fig. 2b, and the compensated errors of angular variation kNθ are shown in Fig. 3.
