The issue of water quality monitoring is becoming a huge area as the EU requirements for cleaner water increase. On-line monitoring involves measuring a body of water constantly and in-situ as opposed to analysing samples in the lab. Currently filling the gap in the market, Wastewater Quality Monitoring: On-line Methods provides information on how to produce the best analyses of wastewater in order to meet the above mentioned requirements. This reference will prove invaluable to all local water companies, industrial companies producing wastewater, as well as environment agencies and researchers.
An advanced discussion of linear models with mixed or random effects. In recent years a breakthrough has occurred in our ability to draw inferences from exact and optimum tests of variance component models, generating much research activity that relies on linear models with mixed and random effects. This volume covers the most important research of the past decade as well as the latest developments in hypothesis testing. It compiles all currently available results in the area of exact and optimum tests for variance component models and offers the only comprehensive treatment for these models at an advanced level. Statistical Tests for Mixed Linear Models: Combines analysis and testing in one self-contained volume. Describes analysis of variance (ANOVA) procedures in balanced and unbalanced data situations. Examines methods for determining the effect of imbalance on data analysis. Explains exact and optimum tests and methods for their derivation. Summarizes test procedures for multivariate mixed and random models. Enables novice readers to skip the derivations and discussions on optimum tests. Offers plentiful examples and exercises, many of which are numerical in flavor. Provides solutions to selected exercises. Statistical Tests for Mixed Linear Models is an accessible reference for researchers in analysis of variance, experimental design, variance component analysis, and linear mixed models. It is also an important text for graduate students interested in mixed models.