By Robert D. Gibbons
Content material:
Chapter 1 basic Prediction durations (pages 1–33):
Chapter 2 Nonparametric Prediction durations (pages 35–66):
Chapter three Prediction durations for different Distributions (pages 67–76):
Chapter four Gamma Prediction durations and a few similar themes (pages 77–95):
Chapter five Tolerance durations (pages 97–110):
Chapter 6 technique Detection Limits (pages 111–136):
Chapter 7 sensible Quantitation Limits (pages 137–145):
Chapter eight Interlaboratory Calibration (pages 147–159):
Chapter nine Contaminant resource research (pages 161–189):
Chapter 10 Intra?Well comparability (pages 191–204):
Chapter eleven pattern research (pages 205–215):
Chapter 12 Censored info (pages 217–244):
Chapter thirteen common Prediction Limits for Left?Censored information (pages 245–255):
Chapter 14 exams for Departure From Normality (pages 257–280):
Chapter 15 Variance part versions (pages 281–288):
Chapter sixteen Detecting Outliers (pages 289–301):
Chapter 17 floor Water research (pages 303–315):
Chapter 18 evaluate and Corrective motion tracking (pages 317–335):
Chapter 19 Regulatory matters (pages 337–349):
Chapter 20 precis (pages 351–352):
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Extra resources for Statistical Methods for Groundwater Monitoring
Example text
Second, the tables provide values of K for background sample sizes of n = 4 to 100 and k = 5 to 50 monitoring wells. Third, separate tables are prepared for one resample and two resamples under Plans A (pass of all resamples) and B (pass at least one resample). In this way, these important results can be practically applied to the widest variety of monitoring programs. 0025. The importance of disentangling the number of monitoring wells from the number of constituents has often been poorly understood in practice.
7%. 004% were predicted. These discrepancies are the opposite of what we observed in the case without resampling. The reason for the increased observed false positive rate is that by chance alone, background levels may be particularly low for one well and constituent, but it is this same low background being compared to the verification resample(s). As such, the probability of two successive failures is not the simple product of the individual probabilities, as assumed by the Bonferroni adjustment.
NORMAL PREDICTION LIMITS WITH RESAMPLING 9 As a more conservative alternative, consider a monitoring program, which, in the event of an initial exceedance, two verification resamples are obtained and a significant exceedance is recorded if either resampled value exceeds the limit. , the product of failing an initial sample and at least one of the two verification resamples. 05) within budgetary, legislative, and independence constraints. , site-wide false positive rate of approximately 5% and an individual test false positive rate of approximately 1 %).