03640oam 2200733Mn 450 991080018400332120230810000340.00-429-15764-91-138-47516-51-4822-2428-3(CKB)2670000000557360(EBL)1524332(SSID)ssj0001222149(PQKBManifestationID)11771411(PQKBTitleCode)TC0001222149(PQKBWorkID)11194199(PQKB)10098873(OCoLC)882257800(MiAaPQ)EBC1524332(OCoLC)1080588961(OCoLC-P)1080588961(FlBoTFG)9780429157646(EXLCZ)99267000000055736020190103d2017 uy 0engur|n|||||||||txtccrMathematics manual for water and wastewater treatment plant operators wastewater treatment operations : math concepts and calculations /Frank R. Spellman2nd edition.Boca Raton, FL CRC Press20171 online resource (256 p.) illustrationsMathematics manual for water and wastewater treatment plant operators Mathematics for water and wastewater treatment plant operatorsDescription based upon print version of record.1-306-86608-1 1-4822-2429-1 Front Cover; Contents; Preface; Author; Chapter 1: Flow, Velocity, and Pumping Calculations; Chapter 2: Preliminary Treatment Calculations; Chapter 3: Primary Treatment Calculations; Chapter 4: Trickling Filter Calculations; Chapter 5: Rotating Biological Contactors; Chapter 6: Activated Biosolids; Chapter 7: Treatment Ponds; Chapter 8: Chemical Dosage; Chapter 9: Biosolids Production and Pumping Calculations; Chapter 10: Biosolids Thickening Calculations; Chapter 11: Biosolids Digestion; Chapter 12: Biosolids Dewatering and Disposal; Chapter 13: Water/Wastewater Laboratory CalculationsChapter 14: Wastewater Treatment Practice CalculationsAppendix A: Solutions to Chapter 14 Problems; Appendix B: Formulas; Back CoverTo properly operate a waterworks or wastewater treatment plant and to pass the examination for a waterworks/wastewater operator's license, it is necessary to know how to perform certain calculations. All operators, at all levels of licensure, need a basic understanding of arithmetic and problem-solving techniques to solve the problems they typically encounter in the workplace.WaterPurificationMathematicsWater quality managementMathematicsWaterPurificationProblems, exercises, etcWater quality managementProblems, exercises, etcSewagePurificationMathematicsSewage disposalMathematicsSewagePurificationProblems, exercises, etcSewage disposalProblems, exercises, etcWaterPurificationMathematics.Water quality managementMathematics.WaterPurificationWater quality managementSewagePurificationMathematics.Sewage disposalMathematics.SewagePurificationSewage disposal628.101/51Spellman Frank R.477239OCoLC-POCoLC-PBOOK9910800184003321Mathematics manual for water and wastewater treatment plant operators3806855UNINA05776nam 2200793Ia 450 991097228700332120200520144314.097866121683149781282168312128216831297800809120350080912036(CKB)1000000000766628(EBL)452830(OCoLC)500575206(SSID)ssj0000298268(PQKBManifestationID)12098230(PQKBTitleCode)TC0000298268(PQKBWorkID)10343234(PQKB)11677600(Au-PeEL)EBL452830(CaPaEBR)ebr10310724(CaONFJC)MIL216831(PPN)170601579(FR-PaCSA)88811747(CaSebORM)9780080912035(MiAaPQ)EBC452830(FRCYB88811747)88811747(EXLCZ)99100000000076662820090313d2009 uy 0engur|n|---|||||txtrdacontentcrdamediacrrdacarrierHandbook of statistical analysis and data mining applications /Robert Nisbet, John Elder, Gary MinerFirst edition.Amsterdam ;Boston Academic Press/Elsevierc20091 online resource (859 pages)Description based upon print version of record.9780123747655 0123747651 Includes bibliographical references and index.Front Cover; Handbook of Statistical Analysis and Data Mining Applications; Copyright Page; Table of Contents; Foreword 1; Foreword 2; Preface; Introduction; List of Tutorials by Guest Authors; Part 1: History of Phases of Data Analysis, Basic Theory, and the Data Mining Process; Chapter 1: The Background for Data Mining Practice; Assumptions of the Parametric Model; Two Views of Reality; Aristotle; Plato; The Rise of Modern Statistical Analysis: The Second Generation; Machine Learning Methods: The Third Generation; Statistical Learning Theory: The Fourth GenerationChapter 2: Theoretical Considerations for Data MiningMajor Issues in Data Mining; General Requirements for Success in a Data Mining Project; The Importance of Domain Knowledge; Postscript; Some Caveats with Data Mining Solutions; Chapter 3: The Data Mining Process; CRISP-DM; Assess the Business Environment for Data Mining; Data Understanding (Mostly Science); References; Preamble; Chapter 4: Data Understanding and Preparation; Preamble; Issues That Should be Resolved; Splitting DataPart 1: Using a Wrapper Approach in Weka to Determine the Most Appropriate Variables for Your Neural Network ModelExample 4; Data Extraction; Data Weighting and Balancing; Data Filtering and Smoothing; Data Abstraction; Data Reduction; Data Sampling; Data Discretization; Data Derivation; Postscript; Chapter 5: Feature Selection; Inductive Database Approach; Bi-variate Methods; Multivariate Methods; Postscript; Complex Methods; The Other Two Ways of Using Feature Selection in STATISTICA: Interactive Workspace; Preamble; Chapter 6: Accessory Tools for Doing Data Mining; Preamble; IntroductionBasic Descriptive StatisticsCombining Groups (Classes) for Predictive Data Mining; Generalized Linear Models (GLMs); Data Miner Workspace Templates; Comparison of Models with and Without Time-Based Features; Example: The IDP Facility of STATISTICA Data Miner; Ensembles in General; Part 2: The Algorithms in Data Mining and Text Mining, the Organization of the Three most common Data Mining Tools, and Selected Speci...; Chapter 7: Basic Algorithms for Data Mining: A Brief Overview; Preamble; STATISTICA Data Miner Recipe (DMRecipe); Automated Neural Nets; Generalized Additive Models (GAMs)Outputs of GAMsRecursive Partitioning; Pruning Trees; Bibliography; Chapter 8: Advanced Algorithms for Data Mining; The Physical Data Mart; Summary; Micro-Target the Profitable Customers; Quality Control Data Mining and Root Cause Analysis; Chapter 9: Text Mining and Natural Language Processing; The Development of Text Mining; Chapter 10: The Three Most Common Data Mining Software Tools; Preamble; SPSS Clementine Overview; Preamble; Setting the Default Directory; Visual Data Preparation for Data Mining: Taking Photos, Moving Pictures, and Objects into Spreadsheets Representing the Photos...PreambleThe Handbook of Statistical Analysis and Data Mining Applications is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers (both academic and industrial) through all stages of data analysis, model building and implementation. The Handbook helps one discern the technical and business problem, understand the strengths and weaknesses of modern data mining algorithms, and employ the right statistical methods for practical application. Use this book to address massive and complex datasets with novel statistical approaches and be aHandbook of statistical analysis & data mining applicationsData miningStatistical methodsMultivariate analysisData miningStatistical methods.Multivariate analysis.006.3/12 22519.5006.31201519531.73bclNisbet Robert142531Elder John F(John Fletcher)783974Miner Gary322168MiAaPQMiAaPQMiAaPQBOOK9910972287003321Handbook of statistical analysis and data mining applications1741883UNINA