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- Full title: Introduction to Probability and Statistics, 15th Edition
- Edition: 15th
- Copyright year: 2020
- Publisher: Cengage Learning
- Author: William Mendenhall; Robert J. Beaver; Barbara M. Beaver
- ISBN: 9781337554428, 9780357044308
- Format: PDF
Description of Introduction to Probability and Statistics, 15th Edition:
Mendenhall, Beaver, and Beaver’s INTRODUCTION TO PROBABILITY AND STATISTICS, 15th Edition is a major overhaul from the previous edition, lowering the reading level, introducing concepts in a more intuitive way and significantly increasing homework scaffolding for difficulty level. Written in compliance with the GAISE college report, this text teaches students to become problem solvers who are adept at using technology to facilitate statistical reasoning as well as the interpretation of statistical results. Students will be able to describe real sets of data meaningfully, what the statistical tests mean in terms of their practical applications, how to evaluate the validity of the assumptions behind statistical tests and know what to do when statistical assumptions have been violated. The 15th edition contains 1884 exercises, employs real data throughout and includes at least 75% new or updated examples. INTRODUCTION TO PROBABILITY AND STATISTICS, 15th Edition, adds new sections on the uniform and exponential distributions, normal probability plots for assessing normality, best subsets regression procedures and binary logistic regression. With features designed specifically for Statistics, WebAssign helps to address relevant applications, use of technology and conceptual understanding. Use additional material to accompany the text, including: news videos per chapter, pre-made Labs, Project Milestones, Simulation Questions by JMP and Concept Questions. NEW for Fall 2020 – Turn your students into statistical thinkers with the Statistical Analysis and Learning Tool (SALT). SALT is an easy-to-use data analysis tool created with the intro-level student in mind. It contains dynamic graphics and allows students to manipulate data sets in order to visualize statistics and gain a deeper conceptual understanding about the meaning behind data. SALT is built by Cengage, comes integrated in Cengage WebAssign Statistics courses and available to use standalone.Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.
Table of Contents of Introduction to Probability and Statistics, 15th Edition PDF ebook:
ContentsPrefaceIntroduction: What Is Statistics?The Population and the SampleDescriptive and Inferential StatisticsAchieving the Objective of Inferential Statistics: The Necessary StepsKeys for Successful LearningChapter 1: Describing Data with Graphs1.1 Variables and DataTypes of VariablesExercises1.2 Graphs for Categorical DataExercises1.3 Graphs for Quantitative DataPie Charts and Bar ChartsLine ChartsDotplotsStem and Leaf PlotsInterpreting Graphs with a Critical EyeExercises1.4 Relative Frequency HistogramsExercisesChapter ReviewTechnology TodayReviewing What You’ve LearnedCase Study: How Is Your Blood Pressure?Chatper 2: Describing Data with Numerical MeasuresIntroduction2.1 Measures of CenterExercises2.2 Measures of VariabilityExercises2.3 Understanding and Interpreting the Standard DeviationTchebysheff’s TheoremThe Empirical RuleApproximating s Using the RangeExercises2.4 Measures of Relative Standingz-ScoresPercentiles and QuartilesThe Five-Number Summary and the Box PlotExercisesChapter ReviewTechnology TodayReviewing What You’ve LearnedCase Study: The Boys of SummerChatper 3: Describing Bivariate DataIntroduction3.1 Describing Bivariate Categorical DataExercises3.2 Describing Bivariate Quantitative DataScatterplotsThe Correlation CoefficientThe Least-Squares LineExercisesChapter ReviewTechnology TodayReviewing What You’ve LearnedCase Study: Are Your Clothes Really Clean?Chapter 4: ProbabilityIntroduction4.1 Events and the Sample SpaceExercises4.2 Calculating Probabilities Using Simple EventsExercises4.3 Useful Counting RulesUsing the TI-83/84 Plus CalculatorExercises4.4 Rules for Calculating ProbabilitiesCalculating Probabilities for Unions and ComplementsCalculating Probabilities for IntersectionsExercises4.5 Bayes’ RuleExercisesChapter ReviewReviewing What You’ve LearnedCase Study: Probability and Decision Making in the CongoChapter 5: Discrete Probability Distributions5.1 Discrete Random Variables and Their Probability DistributionsRandom VariablesProbability DistributionsThe Mean and Standard Deviation for a Discrete Random VariableExercises5.2 The Binomial Probability DistributionExercises5.3 The Poisson Probability DistributionExercises5.4 The Hypergeometric Probability DistributionExercisesChapter ReviewTechnology TodayReviewing What You’ve LearnedCase Study: A Mystery: Cancers Near a Reactor?Chapter 6: The Normal Probability Distribution6.1 Probability Distributions for Continuous Random VariablesThe Continuous Uniform Probability DistributionThe Exponential Probability DistributionExercises6.2 The Normal Probability DistributionThe Standard Normal Random VariableCalculating Probabilities for a General Normal Random VariableExercises6.3 The Normal Approximation to the Binomial Probability DistributionExercisesChapter ReviewTechnology TodayReviewing What You’ve LearnedCase Study: “Are You Going to Curve the Grades?”Chapter 7: Sampling DistributionsIntroduction7.1 Sampling Plans and Experimental DesignsExercises7.2 Statistics and Sampling DistributionsExercises7.3 The Central Limit Theorem and the Sample MeanThe Central Limit TheoremThe Sampling Distribution of the Sample MeanStandard Error of the Sample MeanExercises7.4 Assessing Normality7.5 The Sampling Distribution of the Sample ProportionExercises7.6 A Sampling Application: Statistical Process Control (Optional)A Control Chart for the Process Mean: The x ChartA Control Chart for the Proportion Defective: The p ChartExercisesChapter ReviewTechnology TodayReviewing What You’ve LearnedCase Study: Sampling the Roulette at Monte CarloChapter 8: Large-Sample Estimation8.1 Where We’ve Been and Where We’re GoingStatistical InferenceTypes of Estimators8.2 Point EstimationExercises8.3 Interval EstimationConstructing a Confidence IntervalLarge-Sample Confidence Interval for a Population MeanInterpreting the Confidence IntervalLarge-Sample Confidence Interval for a Population Proportion pUsing TechnologyExercises8.4 Estimating the Difference Between Two Population MeansExercises8.5 Estimating the Difference Between Two Binomial ProportionsUsing TechnologyExercises8.6 One-Sided Confidence BoundsExercises8.7 Choosing the Sample SizeExercisesChapter ReviewTechnology TodayReviewing What You’ve LearnedCase Study: How Reliable Is That Poll? CBS News: How and Where America EatsChapter 9: Large-Sample Tests of HypothesesIntroduction9.1 A Statistical Test of HypothesisExercises9.2 A Large-Sample Test About a Population MeanThe Essentials of the TestCalculating the p-ValueTwo Types of ErrorsThe Power of a Statistical TestExercises9.3 A Large-Sample Test of Hypothesis for the Difference Between Two Population MeansHypothesis Testing and Confidence IntervalsExercises9.4 A Large-Sample Test of Hypothesis for a Binomial ProportionStatistical Significance and Practical ImportanceExercises9.5 A Large-Sample Test of Hypothesis for the Difference Between Two Binomial ProportionsExercises9.6 Concluding Comments on Testing HypothesesChapter ReviewTechnology TodayReviewing What You’ve LearnedCase Study: An Aspirin a Day . . . ?Chapter 10: Inference from Small SamplesIntroduction10.1 Student’s t DistributionAssumptions behind Student’s t DistributionExercises10.2 Small-Sample Inferences Concerning a Population MeanExercises10.3 Small-Sample Inferences for the Difference Between Two Population Means: Independent Random SamExercises10.4 Small-Sample Inferences for the Difference Between Two Means: A Paired-Difference TestExercises10.5 Inferences Concerning a Population VarianceExercises10.6 Comparing Two Population VariancesExercises10.7 Revisiting the Small-Sample AssumptionsChapter ReviewTechnology TodayReviewing What You’ve LearnedCase Study: School AccountabilityAre We Doing Better?Chapter 11: The Analysis of Variance11.1 The design of an experimentBasic DefinitionsWhat Is an Analysis of Variance?The Assumptions for an Analysis of VarianceExercises11.2 The completely randomized design: A One-Way classificationPartitioning the Total Variation in the ExperimentTesting the Equality of the Treatment MeansEstimating Differences in the Treatment MeansExercises11.3 Ranking Population MeansExercises11.4 The Randomized Block Design: A Two-Way ClassificationPartitioning the Total Variation in the ExperimentTesting the Equality of the Treatment and Block MeansIdentifying Differences in the Treatment and Block MeansSome Cautionary Comments on BlockingExercises11.5 The a x b Factorial Experiment: A Two-Way ClassificationThe Analysis of Variance for an a 3 b Factorial ExperimentExercises11.6 Revisiting the Analysis of Variance AssumptionsResidual Plots11.7 A brief summaryChapter ReviewTechnology TodayReviewing What You’ve LearnedCase Study: How to Save Money on Groceries!Chapter 12: Simple Linear Regression and CorrelationIntroduction12.1 Simple Linear RegressionA Simple Linear ModelThe Method of Least SquaresExercises12.2 An Analysis of Variance for Linear RegressionExercises12.3 Testing the Usefulness of the Linear Regression ModelInferences About , the Slope of the Line of MeansThe Analysis of Variance F-TestMeasuring the Strength of the Relationship: The Coefficient of DeterminationInterpreting the Results of a Significant RegressionExercises12.4 Diagnostic Tools for Checking the Regression AssumptionsDependent Error TermsResidual PlotsExercises12.5 Estimation and Prediction Using the Fitted LineExercises12.6 Correlation AnalysisExercisesChapter ReviewTechnology TodayReviewing What You’ve LearnedCase Study: Is Your Car “Made in the U.S.A.”?Chapter 13: Multiple Linear Regression AnalysisIntroduction13.1 The Multiple Regression Model13.2 Multiple Regression AnalysisThe Method of Least SquaresThe Analysis of VarianceTesting the Usefulness of the Regression ModelInterpreting the Results of a Significant RegressionBest Subsets RegressionChecking the Regression AssumptionsUsing the Regression Model for Estimation and PredictionExercises13.3 A Polynomial Regression ModelExercises13.4 Using Quantitative and Qualitative Predictor Variables in a Regression ModelExercises13.5 Testing Sets of Regression Coefficients13.6 Other Topics in Multiple Linear RegressionInterpreting Residual PlotsStepwise Regression AnalysisBinary Logistic RegressionMisinterpreting a Regression Analysis13.7 Steps to Follow When Building a Multiple Regression ModelChapter ReviewTechnology TodayReviewing What You’ve LearnedCase Study: “Made in the U.S.A.”Another LookChapter 14: Analysis of Categorical Data14.1 The Multinomial Experiment and the Chi-Square Statistic14.2 Testing Specified Cell Probabilities: The Goodness-of-Fit TestExercises14.3 Contingency Tables: A Two-Way ClassificationThe Chi-Square Test of IndependenceExercises14.4 Comparing Several Multinomial Populations: A Two-Way Classification with Fixed Row or Column ToExercises14.5 Other Topics in Categorical Data AnalysisThe Equivalence of Statistical TestsOther Applications of the Chi-Square TestChapter ReviewTechnology TodayReviewing What You’ve LearnedCase Study: Who Is the Primary Breadwinner in Your Family?Chapter 15: Nonparametric StatisticsIntroduction15.1 The Wilcoxon Rank Sum Test: Independent Random SamplesNormal Approximation for the Wilcoxon Rank Sum TestExercises15.2 The Sign Test for a Paired ExperimentNormal Approximation for the Sign TestExercises15.3 A Comparison of Statistical Tests15.4 The Wilcoxon Signed-Rank Test for a Paired ExperimentNormal Approximation for the Wilcoxon Signed-Rank TestExercises15.5 The KruskalWallis H-Test for Completely Randomized DesignsExercises15.6 The Friedman Fr-Test for Randomized Block DesignsExercises15.7 Rank Correlation CoefficientExercises15.8 SummaryChapter ReviewTechnology TodayReviewing What You’ve LearnedCase Study: Amazon HQ2Appendix ITable 1 Cumulative Binomial ProbabilitiesTable 2 Cumulative Poisson ProbabilitiesTable 3 Areas under the Normal CurveTable 4 Critical Values of tTable 5 Critical Values of Chi-SquareTable 6 Percentage Points of the F DistributionTable 7 Critical Values of T for the Wilcoxon Rank Sum Test, n1 # n2Table 8 Critical Values of T for the Wilcoxon Signed-Rank Test, n 5 5(1)50Table 9 Critical Values of Spearman’s Rank Correlation Coefficient for a One-Tailed TestTable 10 Random NumbersTable 11 Percentage Points of the Studentized Range, q.05(k, df)Data SourcesAnswers to Selected ExercisesIndex