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Statistics For Environmental Engineers 2Nd Edition 2002 at Meripustak

Statistics For Environmental Engineers 2Nd Edition 2002 by Linfield C. Brown, Paul Mac Berthouex , Taylor & Francis Ltd

Books from same Author: Linfield C. Brown, Paul Mac Berthouex

Books from same Publisher: Taylor & Francis Ltd

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  • General Information  
    Author(s)Linfield C. Brown, Paul Mac Berthouex
    PublisherTaylor & Francis Ltd
    ISBN9781566705929
    Pages512
    BindingHardback
    LanguageEnglish
    Publish YearJanuary 2002

    Description

    Taylor & Francis Ltd Statistics For Environmental Engineers 2Nd Edition 2002 by Linfield C. Brown, Paul Mac Berthouex

    Two critical questions arise when one is confronted with a new problem that involves the collection and analysis of data. How will the use of statistics help solve this problem? Which techniques should be used? Statistics for Environmental Engineers, Second Edition helps environmental science and engineering students answer these questions when the goal is to understand and design systems for environmental protection. The second edition of this bestseller is a solutions-oriented text that encourages students to view statistics as a problem-solving tool. Written in an easy-to-understand style, Statistics for Environmental Engineers, Second Edition consists of 54 short, "stand-alone" chapters. All chapters address a particular environmental problem or statistical technique and are written in a manner that permits each chapter to be studied independently and in any order. Chapters are organized around specific case studies, beginning with brief discussions of the appropriate methodologies, followed by analysis of the case study examples, and ending with comments on the strengths and weaknesses of the approaches. New to this edition:Thirteen new chapters dealing with topics such as experimental design, sizing experiments, tolerance and prediction intervals, time-series modeling and forecasting, transfer function models, weighted least squares, laboratory quality assurance, and specialized control chartsExercises for classroom use or self-study in each chapterImproved graphicsRevisions to all chaptersWhether the topic is displaying data, t-tests, mechanistic model building, nonlinear least squares, confidence intervals, regression, or experimental design, the context is always familiar to environmental scientists and engineers. Case studies are drawn from censored data, detection limits, regulatory standards, treatment plant performance, sampling and measurement errors, hazardous waste, and much more. This revision of a classic text serves as an ideal textbook for students and a valuable reference for any environmental professional working with numbers. Environmental Problems and StatisticsA Brief Review of StatisticsPlotting DataSmoothing DataSeeing the Shape of a DistributionExternal Reference DistributionsUsing TransformationsEstimating PercentilesAccuracy, Bias, and Precision of MeasurementsPrecision of Calculated ValuesLaboratory Quality AssuranceFundamentals of Process Control ChartsSpecialized Control ChartsLimit of DetectionAnalyzing Censored DataComparing a Mean with a StandardPaired t -Test for Assessing the Average of DifferencesIndependent t-Test for Assessing the Difference of Two AveragesAssessing the Difference of ProportionsMultiple Paired Comparison of k AveragesTolerance Intervals and Prediction IntervalsExperimental DesignSizing the ExperimentAnalysis of Variance to Compare k AveragesComponents of VarianceMultiple Factor Analysis of VarianceFactorial Experimental DesignsFractional Factorial Experimental DesignsScreening of Important VariablesAnalyzing Factorial Experiments by RegressionCorrelationSerial CorrelationThe Method of Least SquaresPrecision of Parameter Estimates in Linear ModelsPrecision of Parameter Estimates in Nonlinear ModelsCalibrationWeighted Least SquaresEmpirical Model Building by Linear RegressionThe Coefficient of Determination, R2Regression Analysis with Categorical VariablesThe Effect of Autocorrelation on RegressionThe Iterative Approach to ExperimentationSeeking OptimumConditions by Response Surface MethodologyDesigning Experiments for Nonlinear Parameter EstimationWhy Linearization Can Bias Parameter EstimatesA Problem in Fitting Models to Multiresponse DataModel DiscriminationData Adjustment for Process RationalizationHow Measurement Errors are Transmitted into Calculated ValuesUsing Simulations to Study Statistical ProblemsIntroduction to Time Series ModelingTransfer Function ModelsForecasting Time SeriesIntervention AnalysisAppendix-Statistical TablesIndex



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