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This book explains the statistical methods used to analyze the huge volume of data that groundwater monitoring wells produce in a comprehensive manner accessible to engineers and scientists who may not have a strong background in statistics. In addition, the book provides statistical methods to make the most accurate use of the data and shows how to set up an effective monitoring system.
From the Publisher
This book explains the statistical methods used to analyze the huge volume of data that groundwater monitoring wells produce in a comprehensive manner accessible to engineers and scientists who may not have a strong background in statistics. In addition, the book provides statistical methods to make the most accurate use of the data and shows how to set up an effective monitoring system.
This book explains the statistical methods used to analyze the huge volume of data that groundwater monitoring wells produce in a comprehensive manner accessible to engineers and scientists who may not have a strong background in statistics. In addition, the book provides statistical methods to make the most accurate use of the data and shows how to set up an effective monitoring system.
From the Publisher
This book explains the statistical methods used to analyze the huge volume of data that groundwater monitoring wells produce in a comprehensive manner accessible to engineers and scientists who may not have a strong background in statistics. In addition, the book provides statistical methods to make the most accurate use of the data and shows how to set up an effective monitoring system.
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Inside This Book
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First Sentence:
The fundamental problem in groundwater monitoring is the prediction of future measurements based on a background sample of historical measurements (Gibbons, 1987a; Davis and McNichols, 1987). Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
verification resamples, sitewide false positive rate, groundwater monitoring applications, nonparametric prediction limits, single concentration designs, normal prediction limit, next single measurement, intrawell comparisons, detection limit estimators, two resamples, groundwater detection monitoring, prediction limit factors, single upgradient, upgradient samples, initial exceedance, downgradient comparisons, nonparametric discriminant function, significant exceedance, downgradient monitoring wells, individual slope estimates, background sample sizes, spiking concentration, upgradient wells, upper prediction limit, detection monitoring programs Key Phrases - Capitalized Phrases (CAPs): (learn more)
Tire Pile, South Central, Background Previous Number of Monitoring Wells, Charles Davis, One of Three Samples, Sample Size Upper, Inspection of Figure, Priority Pollutant List Volatile Organics, Monte Carlo New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
The fundamental problem in groundwater monitoring is the prediction of future measurements based on a background sample of historical measurements (Gibbons, 1987a; Davis and McNichols, 1987). Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
verification resamples, sitewide false positive rate, groundwater monitoring applications, nonparametric prediction limits, single concentration designs, normal prediction limit, next single measurement, intrawell comparisons, detection limit estimators, two resamples, groundwater detection monitoring, prediction limit factors, single upgradient, upgradient samples, initial exceedance, downgradient comparisons, nonparametric discriminant function, significant exceedance, downgradient monitoring wells, individual slope estimates, background sample sizes, spiking concentration, upgradient wells, upper prediction limit, detection monitoring programs Key Phrases - Capitalized Phrases (CAPs): (learn more)
Tire Pile, South Central, Background Previous Number of Monitoring Wells, Charles Davis, One of Three Samples, Sample Size Upper, Inspection of Figure, Priority Pollutant List Volatile Organics, Monte Carlo New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
This book cites 31
books:
See all 31 books this book cites
10
books
cite this book:
See all 10 books citing this book
- Biometrika Tables for Statisticians by E. S. Pearson on page 226, page 227, and Back Matter
- Handbook of Statistical Tables (Addison-Wesley Series in Statistics) by Donald B. Owen on page 203, and Back Matter
- Statistical Methods for Environmental Pollution Monitoring by Richard O. Gilbert on page 250, and Back Matter
- Statistical Methods by George W. Snedecor in Back Matter
- The Least One (Library Alabama Classics) by Borden Deal on page 64
See all 31 books this book cites
- Statistics for Environmental Engineers, Second Edition by Paul Mac Berthouex on 6 pages
- Statistical Methods for Detection and Quantification of Environmental Contamination by Robert D. Gibbons on page 268, and page 270
- Environmental Statistics with S-PLUS (Crc Applied Environmental Statistics Series) by Steven P. Millard in Back Matter
- Frontiers in Statistical Quality Control 7 (Pt. 7) by Hans-Joachim Lenz on page 99
- Geoenvironmental Engineering: Principles and Applications (Books in Soils, Plants, and the Environment) by Lakshmi Reddi on page 241
See all 10 books citing this book
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