Solution Manual for Probability and Statistics for Engineers, 5th Edition

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Product details:

• ISBN-10 ‏ : ‎ 0534403026
• ISBN-13 ‏ : ‎ 978-0534403027
• Author: Richard Scheaffer;  Madhuri Mulekar 

PROBABILITY AND STATISTICS FOR ENGINEERS provides a one-semester, calculus-based introduction to engineering statistics that focuses on making intelligent sense of real engineering data and interpreting results. Traditional topics are presented thorough an accessible modern framework that emphasizes the statistical thinking, data collection and analysis, decision-making, and process improvement skills that engineers need on a daily basis to solve real problems. The text continues to be driven by its hallmark array of engineering applications–thoroughly expanded and modernized for the 5th edition–which tackle timely, interesting, and illuminating scenarios that show students the rich context behind the concepts. Within the presentation of topics and applications the authors continually develop students’ intuition for collecting their own real data, analyzing it with the latest graphical tools, and interpreting the results with a goal of improving quality control and problem-solving process. Students will not only gain solid understanding of concepts and their real-life practicality, but will learn to become active statistical practitioners for their own future careers.

Table of contents:

• Dedication
• Preface
  • Organization and Coverage
  • Supplemental Materials
  • Acknowledgments
• Chapter 1. Introduction to Statistics
  • 1.1 Introduction
  • 1.2 Data Collection and Descriptive Statistics
  • 1.3 Inferential Statistics and Probability Models
  • 1.4 Populations and Samples
  • 1.5 A Brief History of Statistics
  • Problems
• Chapter 2. Descriptive Statistics
  • 2.1 Introduction
  • 2.2 Describing Data Sets
  • 2.3 Summarizing Data Sets
  • 2.4 Chebyshev’s Inequality
  • 2.5 Normal Data Sets
  • 2.6 Paired Data Sets and the Sample Correlation Coefficient
  • Problems
• Chapter 3. Elements of Probability
  • 3.1 Introduction
  • 3.2 Sample Space and Events
  • 3.3 Venn Diagrams and the Algebra of Events
  • 3.4 Axioms of Probability
  • 3.5 Sample Spaces Having Equally Likely Outcomes
  • 3.6 Conditional Probability
  • 3.7 Bayes’ Formula
  • 3.8 Independent Events
  • Problems
• Chapter 4. Random Variables and Expectation
  • 4.1 Random Variables
  • 4.2 Types of Random Variables
  • 4.3 Jointly Distributed Random Variables
  • 4.4 Expectation
  • 4.5 Properties of the Expected Value
  • 4.6 Variance
  • 4.7 Covariance and Variance of Sums of Random Variables
  • 4.8 Moment Generating Functions
  • 4.9 Chebyshev’s Inequality and the Weak Law of Large Numbers
  • Problems
• Chapter 5. Special Random Variables
  • 5.1 The Bernoulli and Binomial Random Variables
  • 5.2 The Poisson Random Variable
  • 5.3 The Hypergeometric Random Variable
  • 5.4 The Uniform Random Variable
  • 5.5 Normal Random Variables
  • 5.6 Exponential Random Variables
  • 5.7 The Gamma Distribution
  • 5.8 Distributions Arising from the Normal
  • 5.9 The Logistics Distribution
  • Problems
• Chapter 6. Distributions of Sampling Statistics
  • 6.1 Introduction
  • 6.2 The Sample Mean
  • 6.3 The Central Limit Theorem
  • 6.4 The Sample Variance
  • 6.5 Sampling Distributions from a Normal Population
  • 6.6 Sampling from a Finite Population
  • Problems
• Chapter 7. Parameter Estimation
  • 7.1 Introduction
  • 7.2 Maximum Likelihood Estimators
  • 7.3 Interval Estimates
  • 7.4 Estimating the Difference in Means of Two Normal Populations
  • 7.5 Approximate Confidence Interval for the Mean of a Bernoulli Random Variable
  • 7.6 Confidence Interval of the Mean of the Exponential Distribution
  • 7.7 Evaluating a Point Estimator
  • 7.8 The Bayes Estimator
  • Problems
• Chapter 8. Hypothesis Testing
  • 8.1 Introduction
  • 8.2 Significance Levels
  • 8.3 Tests Concerning the Mean of a Normal Population
  • 8.4 Testing The Equality of Means of Two Normal Populations
  • 8.5 Hypothesis Tests Concerning the Variance of a Normal Population
  • 8.6 Hypothesis Tests in Bernoulli Populations
  • 8.7 Tests Concerning the Mean of a Poisson Distribution
  • Problems
• Chapter 9. Regression
  • 9.1 Introduction
  • 9.2 Least Squares Estimators of the Regression Parameters
  • 9.3 Distribution of the Estimators
  • 9.4 Statistical Inferences about the Regression Parameters
  • 9.5 The Coefficient of Determination and the Sample Correlation Coefficient
  • 9.6 Analysis of Residuals: Assessing the Model
  • 9.7 Transforming to Linearity
  • 9.8 Weighted Least Squares
  • 9.9 Polynomial Regression
  • 9.10 Multiple Linear Regression
  • 9.11 Logistic Regression Models for Binary Output Data
  • Problems
• Chapter 10. Analysis of Variance
  • 10.1 Introduction
  • 10.2 An Overview
  • 10.3 One-Way Analysis of Variance
  • 10.4 Two-Factor Analysis of Variance: Introduction and Parameter Estimation
  • 10.5 Two-Factor Analysis of Variance: Testing Hypotheses
  • 10.6 Two-Way Analysis of Variance with Interaction
  • Problems
• Chapter 11. Goodness of Fit Tests and Categorical Data Analysis
  • 11.1 Introduction
  • 11.2 Goodness of Fit Tests When All Parameters are Specified
  • 11.3 Goodness of Fit Tests When Some Parameters are Unspecified
  • 11.4 Tests of Independence in Contingency Tables
  • 11.5 Tests of Independence in Contingency Tables Having Fixed Marginal Totals
  • 11.6 The Kolmogorov–Smirnov Goodness of Fit Test for Continuous Data
  • Problems
• Chapter 12. Nonparametric Hypothesis Tests
  • 12.1 Introduction
  • 12.2 The Sign Test
  • 12.3 The Signed Rank Test
  • 12.4 The Two-Sample Problem
  • 12.5 The Runs Test for Randomness
  • Problems
• Chapter 13. Quality Control
  • 13.1 Introduction
  • 13.2 Control Charts for Average Values: The X¯ Control Chart
  • 13.3 S-Control Charts
  • 13.4 Control Charts for the Fraction Defective
  • 13.5 Control Charts for Number of Defects
  • 13.6 Other Control Charts for Detecting Changes in the Population Mean
  • Problems
• Chapter 14. Life Testing
  • 14.1 Introduction
  • 14.2 Hazard Rate Functions
  • 14.3 The Exponential Distribution In Life Testing
  • 14.4 A Two-Sample Problem
  • 14.5 The Weibull Distribution in Life Testing
  • Problems
• Chapter 15. Simulation, Bootstrap Statistical Methods, and Permutation Tests
  • 15.1 Introduction
  • 15.2 Random Numbers
  • 15.3 The Bootstrap Method
  • 15.4 Permutation Tests
  • 15.4.1 Normal Approximations in Permutation Tests
  • 15.5 Generating Discrete Random Variables
  • 15.6 Generating Continuous Random Variables
  • 15.7 Determining the Number of Simulation Runs in a Monte Carlo Study
  • Problems
• Appendix of Tables
• Index

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