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DescriptionFor junior/senior undergraduates taking probability and statistics as applied to engineering, science, or computer science.This classic text provides a rigorous introduction to basic probability theory and statistical inference, with a unique balance between theory and methodology. Interesting, relevant applications use real data from actual studies, showing how the concepts and methods can be used to solve problems in the field. This revision focuses on improved clarity and deeper understanding.This latest edition is also available in as an enhanced Pearson eText. This exciting new version features an embedded version of StatCrunch, allowing students to analyze data sets while reading the book.Key FeaturesThe balance between theory and applications offers mathematical support to enhance coverage when necessary, giving engineers and scientists the proper mathematical context for statistical tools and methods.Mathematical level: this text assumes one semester of differential and integral calculus as a prerequisite.Calculus is confined to elementary probability theory and probability distributions (Chapters 2—7).Matrix algebra is used modestly in coverage of linear regression material (Chapters 11—12).Linear algebra and the use of matrices are applied in Chapters 11—15, where treatment of linear regression and analysis of variance is covered.Compelling exercise sets challenge students to use the concepts to solve problems that occur in many real-life scientific and engineering situations. Many exercises contain real data from studies in the fields of biomedical, bioengineering, business, computing, etc.Real-life applications of the Poisson, binomial, and hypergeometric distributions generate student interest using topics such as flaws in manufactured copper wire, highway potholes, hospital patient traffic, airport luggage screening, and homeland security.Statistical software coverage in the following case studies includes SAS® and MINITAB®, with screenshots and graphics as appropriate:Two-sample hypothesis testingMultiple linear regressionAnalysis of varianceUse of two-level factorial-experimentsInteraction plots provide examples of scientific interpretations and new exercises using graphics.Topic outlineChapter 1: elementary overview of statistical inferenceChapters 2—4: basic probability; discrete and continuous random variablesChapters 2—10: probability distributions and statistical inferencesChapters 5—6: specific discrete and continuous distributions with illustrations of their use and relationships among themChapter 7: optional chapter covering the transformation of random variables.Chapter 8: additional materials on graphical methods; an important introduction to the notion of sampling distributionChapters 9—10: one and two sample point and interval estimationChapters 11—15: linear regression; analysis of varianceNew to this EditionRevised text focuses on improved clarity and deeper understanding rather than adding extraneous new material.End-of-chapter material strengthens the connections between chapters.“Pot Holes” comments remind students of the bigger picture and how each chapter fits into that picture. These notes also discuss limitations of specific procedures and help students avoid pitfalls in misusing statistics.Class projects in several chapters provide the opportunity for students to gather their own experimental data and draw inferences from that data. These projects illustrate the meaning of a concept or provide empirical understanding of important statistical results, and are suitable for either group or individual work.Case studies provide deeper insight into the practicality of the concepts.Table of ContentsPrefaceIntroduction to Statistics and Data AnalysisOverview: Statistical Inference, Samples, Populations, and the Role of ProbabilitySampling Procedures; Collection of DataMeasures of Location: The Sample Mean and MedianExercisesMeasures of VariabilityExercisesDiscrete and Continuous DataStatistical Modeling, Scientific Inspection, and Graphical Methods 19General Types of Statistical Studies: Designed Experiment,Observational Study, and Retrospective StudyExercisesProbabilitySample SpaceEventsExercisesCounting Sample PointsExercisesProbability of an EventAdditive RulesExercisesConditional Probability, Independence and Product RulesExercisesBayes’ RuleExercisesReview ExercisesPotential Misconceptions and Hazards; Relationship to Material in Other ChaptersRandom Variables and Probability DistributionsConcept of a Random VariableDiscrete Probability DistributionsContinuous Probability DistributionsExercisesJoint Probability DistributionsExercisesReview ExercisesPotential Misconceptions and Hazards; Relationship to Material in Other ChaptersMathematical ExpectationMean of a Random VariableExercisesVariance and Covariance of Random VariablesExercisesMeans and Variances of Linear Combinations of Random Variables 127Chebyshev’s TheoremExercisesReview ExercisesPotential Misconceptions and Hazards; Relationship to Material in Other ChaptersSome Discrete Probability DistributionsIntroduction and MotivationBinomial and Multinomial DistributionsExercisesHypergeometric DistributionExercisesNegative Binomial and Geometric DistributionsPoisson Distribution and the Poisson ProcessExercisesReview ExercisesPotential Misconceptions and Hazards; Relationship to Material in Other ChaptersSome Continuous Probability DistributionsContinuous Uniform DistributionNormal DistributionAreas under the Normal CurveApplications of the Normal DistributionExercisesNormal Approximation to the BinomialExercisesGamma and Exponential DistributionsChi-Squared DistributionBeta DistributionLognormal Distribution (Optional)Weibull Distribution (Optional)ExercisesReview ExercisesPotential Misconceptions and Hazards; Relationship to Material in Other ChaptersFunctions of Random Variables (Optional)IntroductionTransformations of VariablesMoments and Moment-Generating FunctionsExercisesSampling Distributions and More Graphical ToolsRandom Sampling and Sampling DistributionsSome Important StatisticsExercisesSampling DistributionsSampling Distribution of Means and the Central Limit TheoremExercisesSampling Distribution of S2t-DistributionF-DistributionQuantile and Probability PlotsExercisesReview ExercisesPotential Misconceptions and Hazards; Relationship to Material in Other ChaptersOne- and Two-Sample Estimation ProblemsIntroductionStatistical InferenceClassical Methods of EstimationSingle Sample: Estimating the MeanStandard Error of a Point EstimatePrediction IntervalsTolerance LimitsExercisesTwo Samples: Estimating the Difference Between Two MeansPaired ObservationsExercisesSingle Sample: Estimating a ProportionTwo Samples: Estimating the Difference between Two ProportionsExercisesSingle Sample: Estimating the VarianceTwo Samples: Estimating the Ratio of Two VariancesExercisesMaximum Likelihood Estimation (Optional)ExercisesReview ExercisesPotential Misconceptions and Hazards; Relationship to Material in Other ChaptersOne- and Two-Sample Tests of HypothesesStatistical Hypotheses: General ConceptsTesting a Statistical HypothesisThe Use of P-Values for Decision Making in Testing HypothesesExercisesSingle Sample: Tests Concerning a Single MeanTwo Samples: Tests on Two MeansChoice of Sample Size for Testing MeansGraphical Methods for Comparing MeansExercisesOne Sample: Test on a Single ProportionTwo Samples: Tests on Two ProportionsExercisesOne- and Two-Sample Tests Concerning VariancesExercisesGoodness-of-Fit TestTest for Independence (Categorical Data)Test for HomogeneityTwo-Sample Case StudyExercisesReview ExercisesPotential Misconceptions and Hazards; Relationship to Material in Other ChaptersSimple Linear Regression and CorrelationIntroduction to Linear RegressionThe Simple Linear Regression ModelLeast Squares and the Fitted ModelExercisesProperties of the Least Squares EstimatorsInferences Concerning the Regression CoefficientsPredictionExercisesChoice of a Regression ModelAnalysis-of-Variance ApproachTest for Linearity of Regression: Data with Repeated Observations 416ExercisesData Plots and TransformationsSimple Linear Regression Case StudyCorrelationExercisesReview ExercisesPotential Misconceptions and Hazards; Relationship to Material in Other ChaptersMultiple Linear Regression and Certain Nonlinear Regression ModelsIntroductionEstimating the CoefficientsLinear Regression Model Using MatricesExercisesProperties of the Least Squares EstimatorsInferences in Multiple Linear RegressionExercisesChoice of a Fitted Model through Hypothesis TestingSpecial Case of Orthogonality (Optional)ExercisesCategorical or Indicator VariablesExercisesSequential Methods for Model SelectionStudy of Residuals and Violation of AssumptionsCross Validation, Cp, and Other Criteria for Model SelectionExercisesSpecial Nonlinear Models for Nonideal ConditionsExercisesReview ExercisesPotential Misconceptions and Hazards; Relationship to Material in Other ChaptersOne-Factor Experiments: GeneralAnalysis-of-Variance TechniqueThe Strategy of Experimental DesignOne-Way Analysis of Variance: Completely Randomized Design (One-Way ANOVA)Tests for the Equality of Several VariancesExercisesMultiple ComparisonsExercisesComparing a Set of Treatments in BlocksRandomized Complete Block DesignsGraphical Methods and Model CheckingData Transformations In Analysis of Variance)ExercisesRandom Effects ModelsCase StudyExercisesReview ExercisesPotential Misconceptions and Hazards; Relationship to Material in Other ChaptersFactorial Experiments (Two or More Factors)IntroductionInteraction in the Two-Factor ExperimentTwo-Factor Analysis of VarianceExercisesThree-Factor ExperimentsExercisesFactorial Experiments for Random Effects and Mixed ModelsExercisesReview ExercisesPotential Misconceptions and Hazards; Relationship to Material in Other Chapters2k Factorial Experiments and FractionsIntroductionThe 2k Factorial: Calculation of Effects and Analysis of Variance 598Nonreplicated 2k Factorial ExperimentExercisesFactorial Experiments in a Regression SettingThe Orthogonal DesignExercisesFractional Factorial ExperimentsAnalysis of Fractional Factorial ExperimentsExercisesHigher Fractions and Screening DesignsConstruction of Resolution III and IV DesignsOther Two-Level Resolution III Designs; The Plackett-Burman DesignsIntroduction to Response Surface MethodologyRobust Parameter DesignExercisesReview ExercisesPotential Misconceptions and Hazards; Relationship to Material in Other ChaptersNonparametric StatisticsNonparametric TestsSigned-Rank TestExercisesWilcoxon Rank-Sum TestKruskal-Wallis TestExercisesRuns TestTolerance LimitsRank Correlation CoefficientExercisesReview ExercisesStatistical Quality ControlIntroductionNature of the Control LimitsPurposes of the Control ChartControl Charts for VariablesControl Charts for AttributesCusum Control ChartsReview ExercisesBayesian StatisticsBayesian ConceptsBayesian InferencesBayes Estimates Using Decision Theory FrameworkExercisesBibliographyA. Statistical Tables and ProofsB. Answers to Odd-Numbered Non-Review ExercisesIndex