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• Front Cover
• Introduction to Probability and Statistics for Engineers and Scientists
• Copyright Page
• Table of Contents
• Preface
• 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
• 2.2.1 Frequency Tables and Graphs
• 2.2.2 Relative Frequency Tables and Graphs
• 2.2.3 Grouped Data, Histograms, Ogives, and Stem and Leaf Plots
• 2.3 Summarizing Data Sets
• 2.3.1 Sample Mean, Sample Median, and Sample Mode
• 2.3.2 Sample Variance and Sample Standard Deviation
• 2.3.3 Sample Percentiles and Box Plots
• 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
• 4.3.1 Independent Random Variables
• *4.3.2 Conditional Distributions
• 4.4 Expectation
• 4.5 Properties of the Expected Value
• 4.5.1 Expected Value of Sums of Random Variables
• 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
• 5.1.1 Computing the Binomial Distribution Function
• 5.2 The Poisson Random Variable
• 5.2.1 Computing the Poisson Distribution Function
• 5.3 The Hypergeometric Random Variable
• 5.4 The Uniform Random Variable
• 5.5 Normal Random Variables
• 5.6 Exponential Random Variables
• *5.6.1 The Poisson Process
• *5.7 The Gamma Distribution
• 5.8 Distributions Arising from the Normal
• 5.8.1 The Chi-Square Distribution
• 5.8.2 The t-Distribution
• 5.8.3 The F-Distribution
• *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
• 6.3.1 Approximate Distribution of the Sample Mean
• 6.3.2 How Large a Sample Is Needed?
• 6.4 The Sample Variance
• 6.5 Sampling Distributions from a Normal Population
• 6.5.1 Distribution of the Sample Mean
• 6.5.2 Joint Distribution of X and S2
• 6.6 Sampling from a Finite Population
• Problems
• Chapter 7. Parameter Estimation
• 7.1 Introduction
• 7.2 Maximum Likelihood Estimators
• *7.2.1 Estimating Life Distributions
• 7.3 Interval Estimates
• 7.3.1 Confidence Interval for a Normal Mean When the Variance Is Unknown
• 7.3.2 Confidence Intervals for the Variance of a Normal Distribution
• 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
• 8.3.1 Case of Known Variance
• 8.3.2 Case of Unknown Variance: The t-Test
• 8.4 Testing the Equality of Means of Two Normal Populations
• 8.4.1 Case of Known Variances
• 8.4.2 Case of Unknown Variances
• 8.4.3 Case of Unknown and Unequal Variances
• 8.4.4 The Paired t-Test
• 8.5 Hypothesis Tests Concerning the Variance of a Normal Population
• 8.5.1 Testing for the Equality of Variances of Two Normal Populations
• 8.6 Hypothesis Tests in Bernoulli Populations
• 8.6.1 Testing the Equality of Parameters in Two Bernoulli Populations
• 8.7 Tests Concerning the Mean of a Poisson Distribution
• 8.7.1 Testing the Relationship Between Two Poisson Parameters
• 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
• 9.4.1 Inferences Concerning β
• 9.4.2 Inferences Concerning α
• 9.4.3 Inferences Concerning the Mean Response α+βx0
• 9.4.4 Prediction Interval of a Future Response
• 9.4.5 Summary of Distributional Results
• 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
• 9.10.1 Predicting Future Responses
• 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
• 10.3.1 Multiple Comparisons of Sample Means
• 10.3.2 One-Way Analysis of Variance with Unequal Sample Sizes
• 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
• 11.2.1 Determining the Critical Region by Simulation
• 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
• *12.4.1 The Classical Approximation and Simulation
• 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
• 13.2.1 Case of Unknown μ and σ
• 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
• 13.6.1 Moving-Average Control Charts
• 13.6.2 Exponentially Weighted Moving-Average Control Charts
• 13.6.3 Cumulative Sum Control Charts
• Problems
• Chapter 14*. Life Testing
• 14.1 Introduction
• 14.2 Hazard Rate Functions
• 14.3 The Exponential Distribution in Life Testing
• 14.3.1 Simultaneous Testing — Stopping at the rth Failure
• 14.3.2 Sequential Testing
• 14.3.3 Simultaneous Testing — Stopping by a Fixed Time
• 14.3.4 The Bayesian Approach
• 14.4 A Two-Sample Problem
• 14.5 The Weibull Distribution in Life Testing
• 14.5.1 Parameter Estimation by Least Squares
• Problems
• Chapter 15. Simulation, Bootstrap Statistical Methods, and Permutation Tests
• 15.1 Introduction
• 15.2 Random Numbers
• 15.2.1 The Monte Carlo Simulation Approach
• 15.3 The Bootstrap Method
• 15.4 Permutation Tests
• 15.4.1 Normal Approximations in Permutation Tests
• 15.4.2 Two-Sample Permutation Tests
• 15.5 Generating Discrete Random Variables
• 15.6 Generating Continuous Random Variables
• 15.6.1 Generating a Normal Random Variable
• 15.7 Determining the Number of Simulation Runs in a Monte Carlo Study
• Problems
• Appendix of Tables
• Index