Random Phenomena: Fundamentals of Probability and Statistics for Engineers
Babatunde A. Ogunnaike · CERN Document Server (European Organization for Nuclear Research) · 2009
PreludeApproach PhilosophyFour Basic PrinciplesI FoundationsTwo Motivating ExamplesYield Improvement in a Chemical ProcessQuality Assurance in a Glass Sheet Manufacturing ProcessOutline of a Systematic ApproachRandom Phenomena, Variability, and UncertaintyTwo Extreme Idealizations of Natural PhenomenaRandom Mass PhenomenaIntroducing ProbabilityThe Probabilistic FrameworkII ProbabilityFundamentals of Probability TheoryBuilding BlocksOperationsProbabilityConditional ProbabilityIndependenceRandom Variables and DistributionsDistributionsMathematical ExpectationCharacterizing DistributionsSpecial Derived Probability FunctionsMultidimensional Random VariablesDistributions of Several Random VariablesDistributional Characteristics of Jointly Distributed Random VariablesRandom Variable TransformationsSingle Variable TransformationsBivariate TransformationsGeneral Multivariate TransformationsApplication Case Studies I: ProbabilityMendel and HeredityWorld War II Warship Tactical Response Under AttackIII DistributionsIdeal Models of Discrete Random VariablesThe Discrete Uniform Random VariableThe Bernoulli Random VariableThe Hypergeometric Random VariableThe Binomial Random VariableExtensions and Special Cases of the Binomial Random VariableThe Poisson Random VariableIdeal Models of Continuous Random VariablesGamma Family Random VariablesGaussian Family Random VariablesRatio Family Random VariablesInformation, Entropy, and Probability ModelsUncertainty and InformationEntropyMaximum Entropy Principles for Probability ModelingSome Maximum Entropy ModelsMaximum Entropy Models from General ExpectationsApplication Case Studies II: In-Vitro FertilizationIn-Vitro Fertilization and Multiple BirthsProbability Modeling and AnalysisBinomial Model ValidationProblem Solution: Model-Based IVF Optimization and AnalysisSensitivity AnalysisIV StatisticsIntroduction to StatisticsFrom Probability to StatisticsVariable and Data TypesGraphical Methods of Descriptive StatisticsNumerical DescriptionsSamplingThe Distribution of Functions of Random VariablesSampling Distribution of the MeanSampling Distribution of the VarianceEstimationCriteria for Selecting EstimatorsPoint Estimation MethodsPrecision of Point EstimatesInterval EstimatesBayesian EstimationHypothesis TestingBasic ConceptsConcerning Single Mean of a Normal PopulationConcerning Two Normal Population MeansDetermining β, Power, and Sample SizeConcerning Variances of Normal PopulationsConcerning ProportionsConcerning Non-Gaussian PopulationsLikelihood Ratio TestsDiscussionRegression AnalysisSimple Linear Regression"Intrinsically" Linear RegressionMultiple Linear RegressionPolynomial RegressionProbability Model ValidationProbability PlotsChi-Squared Goodness-of-Fit TestNonparametric MethodsSingle PopulationTwo PopulationsProbability Model ValidationA Comprehensive Illustrative ExampleDesign of ExperimentsAnalysis of VarianceSingle Factor ExperimentsTwo-Factor ExperimentsGeneral Multi-factor Experiments2k Factorial Experiments and DesignScreening Designs: Fractional FactorialScreening Designs: Plackett-Burman1Response Surface MethodologyIntroduction to Optimal DesignsApplication Case Studies III: StatisticsPrussian Army Death-by-Horse KicksWW II Aerial Bombardment of LondonUS Population Dynamics: 1790-2000Process OptimizationV ApplicationsReliability and Life TestingSystem ReliabilitySystem Lifetime and Failure-Time DistributionsThe Exponential Reliability ModelThe Weibull Reliability ModelLife TestingQuality Assurance and ControlAcceptance SamplingProcess and Quality ControlChemical Process ControlProcess and Parameter DesignIntroduction to Multivariate AnalysisMultivariate Probability ModelsMultivariate Data AnalysisPrincipal Components AnalysisAppendixIndex.