Contents:

Summary: Praise for the First Edition "This is a well-written and impressively presented introduction to probability and statistics. The text throughout is highly readable, and the author makes liberal use of graphs and diagrams to clarify the theory." - The Statistician Thoroughly updated, Probability: An Introduction with Statistical Applications, Second Edition features a comprehensive exploration of statistical data analysis as an application of probability. The new edition provides an introduction to statistics with accessible coverage of reliability, acceptance sampling, confidence intervals, hypothesis testing, and simple linear regression. Encouraging readers to develop a deeper intuitive understanding of probability, the author presents illustrative geometrical presentations and arguments without the need for rigorous mathematical proofs. The Second Edition features interesting and practical examples from a variety of engineering and scientific fields, as well as: Over 880 problems at varying degrees of difficulty allowing readers to take on more challenging problems as their skill levels increase Chapter-by-chapter projects that aid in the visualization of probability distributions New coverage of statistical quality control and quality production An appendix dedicated to the use of Mathematica® and a companion website containing the referenced data sets Featuring a practical and real-world approach, this textbook is ideal for a first course in probability for students majoring in statistics, engineering, business, psychology, operations research, and mathematics. Probability: An Introduction with Statistical Applications, Second Edition is also an excellent reference for researchers and professionals in any discipline who need to make decisions based on data as well as readers interested in learning how to accomplish effective decision makingSummary: from data.
Cover -- Title Page -- Copyright -- Dedication -- Preface for the First Edition -- Historical Note -- About the Text -- For the Instructor -- Preface for the Second Edition -- Chapter 1: Sample Spaces and Probability -- 1.1 Discrete Sample Spaces -- 1.2 Events -- Axioms of Probability -- 1.3 Probability Theorems -- 1.4 Conditional Probability and Independence -- 1.5 Some Examples -- 1.6 Reliability of Systems -- 1.7 Counting Techniques -- 1.8 Chapter Review -- 1.9 PROBLEMS FOR REVIEW -- Chapter 2: Discrete Random Variables and Probability Distributions -- 2.1 Random Variables -- 2.2 Distribution Functions -- 2.3 Expected Values of Discrete Random Variables -- 2.4 Binomial Distribution -- 2.5 A Recursion -- 2.6 Some Statistical Considerations -- 2.7 Hypothesis Testing: Binomial Random Variables -- 2.8 Distribution of A Sample Proportion -- 2.9 Geometric and Negative Binomial Distributions -- 2.10 The Hypergeometric Random Variable: Acceptance Sampling -- 2.11 Acceptance Sampling (Continued) -- 2.12 The Hypergeometric Random Variable: Further Examples -- 2.13 The Poisson Random Variable -- 2.14 The Poisson Process -- Chapter Review -- Problems for Review -- Chapter 3: Continuous Random Variables and Probability Distributions -- 3.1 Introduction -- 3.2 Uniform Distribution -- 3.3 Exponential Distribution -- 3.4 Reliability -- 3.5 Normal Distribution -- 3.6 Normal Approximation to the Binomial Distribution -- 3.7 Gamma and Chi-Squared Distributions -- 3.8 Weibull Distribution -- Chapter Review -- Problems For Review -- Chapter 4: Functions of Random Variables -- Generating Functions -- Statistical Applications -- 4.1 Introduction -- 4.2 Some Examples of Functions of Random Variables -- 4.3 Probability Distributions of Functions of Random Variables -- 4.4 Sums of Random Variables I -- 4.5 Generating Functions.

4.6 Some Properties of Generating Functions -- 4.7 Probability Generating Functions for Some Specific Probability Distributions -- 4.8 Moment Generating Functions -- 4.9 Properties of Moment Generating Functions -- 4.10 Sums of Random Variables-II -- 4.11 The Central Limit Theorem -- 4.12 Weak Law of Large Numbers -- 4.13 Sampling Distribution of the Sample Variance -- 4.14 Hypothesis Tests and Confidence Intervals for a Single Mean -- 4.15 Hypothesis Tests on Two Samples -- 4.16 Least Squares Linear Regression -- 4.17 Quality Control Chart for -- Chapter Review -- Problems for Review -- Chapter 5: Bivariate Probability Distributions -- 5.1 Introduction -- 5.2 Joint and Marginal Distributions -- 5.3 Conditional Distributions and Densities -- 5.4 Expected Values and the Correlation Coefficient -- 5.5 Conditional Expectations -- 5.6 Bivariate Normal Densities -- 5.7 Functions of Random Variables -- CHAPTER REVIEW -- PROBLEMS FOR REVIEW -- Chapter 6: Recursions and Markov Chains -- 6.1 Introduction -- 6.2 Some Recursions and their Solutions -- 6.3 Random Walk and Ruin -- 6.4 Waiting Times for Patterns in Bernoulli Trials -- 6.5 Markov Chains -- CHAPTER REVIEW -- PROBLEMS FOR REVIEW -- Chapter 7: Some Challenging Problems -- 7.1 My Socks and π -- 7.2 Expected Value -- 7.3 Variance -- 7.4 Other "Socks" Problems -- 7.5 Coupon Collection and Related Problems -- 7.6 Conclusion -- 7.7 Jackknifed Regression and the Bootstrap -- 7.8 Cook's Distance -- 7.9 The Bootstrap -- 7.10 On Waldegrave's Problem -- 7.11 Probabilities of Winning -- 7.12 More than Three Players -- 7.13 Conclusion -- 7.14 On Huygen's First Problem -- 7.15 Changing the Sums for the Players -- Bibliography: Where to Learn More -- Appendix A: Use of Mathematica in Probability and Statistics -- Chapter One -- Chapter Two -- Chapter Three -- Chapter Four -- Chapter Five -- Chapter Six.

Appendix B: Answers for Odd-Numbered Exercises -- Chapter 1 -- Chapter 2 -- Chapter 3 -- Chapter 4 -- Chapter 5 -- Chapter 6 -- Appendix C: Standard Normal Distribution -- The Distribution Table -- Chi-Squared Distribution Table -- Index -- End User License Agreement.

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Cover -- Title Page -- Copyright -- Dedication -- Preface for the First Edition -- Historical Note -- About the Text -- For the Instructor -- Preface for the Second Edition -- Chapter 1: Sample Spaces and Probability -- 1.1 Discrete Sample Spaces -- 1.2 Events -- Axioms of Probability -- 1.3 Probability Theorems -- 1.4 Conditional Probability and Independence -- 1.5 Some Examples -- 1.6 Reliability of Systems -- 1.7 Counting Techniques -- 1.8 Chapter Review -- 1.9 PROBLEMS FOR REVIEW -- Chapter 2: Discrete Random Variables and Probability Distributions -- 2.1 Random Variables -- 2.2 Distribution Functions -- 2.3 Expected Values of Discrete Random Variables -- 2.4 Binomial Distribution -- 2.5 A Recursion -- 2.6 Some Statistical Considerations -- 2.7 Hypothesis Testing: Binomial Random Variables -- 2.8 Distribution of A Sample Proportion -- 2.9 Geometric and Negative Binomial Distributions -- 2.10 The Hypergeometric Random Variable: Acceptance Sampling -- 2.11 Acceptance Sampling (Continued) -- 2.12 The Hypergeometric Random Variable: Further Examples -- 2.13 The Poisson Random Variable -- 2.14 The Poisson Process -- Chapter Review -- Problems for Review -- Chapter 3: Continuous Random Variables and Probability Distributions -- 3.1 Introduction -- 3.2 Uniform Distribution -- 3.3 Exponential Distribution -- 3.4 Reliability -- 3.5 Normal Distribution -- 3.6 Normal Approximation to the Binomial Distribution -- 3.7 Gamma and Chi-Squared Distributions -- 3.8 Weibull Distribution -- Chapter Review -- Problems For Review -- Chapter 4: Functions of Random Variables -- Generating Functions -- Statistical Applications -- 4.1 Introduction -- 4.2 Some Examples of Functions of Random Variables -- 4.3 Probability Distributions of Functions of Random Variables -- 4.4 Sums of Random Variables I -- 4.5 Generating Functions.

4.6 Some Properties of Generating Functions -- 4.7 Probability Generating Functions for Some Specific Probability Distributions -- 4.8 Moment Generating Functions -- 4.9 Properties of Moment Generating Functions -- 4.10 Sums of Random Variables-II -- 4.11 The Central Limit Theorem -- 4.12 Weak Law of Large Numbers -- 4.13 Sampling Distribution of the Sample Variance -- 4.14 Hypothesis Tests and Confidence Intervals for a Single Mean -- 4.15 Hypothesis Tests on Two Samples -- 4.16 Least Squares Linear Regression -- 4.17 Quality Control Chart for -- Chapter Review -- Problems for Review -- Chapter 5: Bivariate Probability Distributions -- 5.1 Introduction -- 5.2 Joint and Marginal Distributions -- 5.3 Conditional Distributions and Densities -- 5.4 Expected Values and the Correlation Coefficient -- 5.5 Conditional Expectations -- 5.6 Bivariate Normal Densities -- 5.7 Functions of Random Variables -- CHAPTER REVIEW -- PROBLEMS FOR REVIEW -- Chapter 6: Recursions and Markov Chains -- 6.1 Introduction -- 6.2 Some Recursions and their Solutions -- 6.3 Random Walk and Ruin -- 6.4 Waiting Times for Patterns in Bernoulli Trials -- 6.5 Markov Chains -- CHAPTER REVIEW -- PROBLEMS FOR REVIEW -- Chapter 7: Some Challenging Problems -- 7.1 My Socks and π -- 7.2 Expected Value -- 7.3 Variance -- 7.4 Other "Socks" Problems -- 7.5 Coupon Collection and Related Problems -- 7.6 Conclusion -- 7.7 Jackknifed Regression and the Bootstrap -- 7.8 Cook's Distance -- 7.9 The Bootstrap -- 7.10 On Waldegrave's Problem -- 7.11 Probabilities of Winning -- 7.12 More than Three Players -- 7.13 Conclusion -- 7.14 On Huygen's First Problem -- 7.15 Changing the Sums for the Players -- Bibliography: Where to Learn More -- Appendix A: Use of Mathematica in Probability and Statistics -- Chapter One -- Chapter Two -- Chapter Three -- Chapter Four -- Chapter Five -- Chapter Six.

Appendix B: Answers for Odd-Numbered Exercises -- Chapter 1 -- Chapter 2 -- Chapter 3 -- Chapter 4 -- Chapter 5 -- Chapter 6 -- Appendix C: Standard Normal Distribution -- The Distribution Table -- Chi-Squared Distribution Table -- Index -- End User License Agreement.

Praise for the First Edition "This is a well-written and impressively presented introduction to probability and statistics. The text throughout is highly readable, and the author makes liberal use of graphs and diagrams to clarify the theory." - The Statistician Thoroughly updated, Probability: An Introduction with Statistical Applications, Second Edition features a comprehensive exploration of statistical data analysis as an application of probability. The new edition provides an introduction to statistics with accessible coverage of reliability, acceptance sampling, confidence intervals, hypothesis testing, and simple linear regression. Encouraging readers to develop a deeper intuitive understanding of probability, the author presents illustrative geometrical presentations and arguments without the need for rigorous mathematical proofs. The Second Edition features interesting and practical examples from a variety of engineering and scientific fields, as well as: Over 880 problems at varying degrees of difficulty allowing readers to take on more challenging problems as their skill levels increase Chapter-by-chapter projects that aid in the visualization of probability distributions New coverage of statistical quality control and quality production An appendix dedicated to the use of Mathematica® and a companion website containing the referenced data sets Featuring a practical and real-world approach, this textbook is ideal for a first course in probability for students majoring in statistics, engineering, business, psychology, operations research, and mathematics. Probability: An Introduction with Statistical Applications, Second Edition is also an excellent reference for researchers and professionals in any discipline who need to make decisions based on data as well as readers interested in learning how to accomplish effective decision making

from data.

Description based on publisher supplied metadata and other sources.

Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2019. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.

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