Introduction to Probability and Statistics for Engineers and Scientists – Sheldon M. Ross – 5th Edition


Introduction to Probability and Statistics for Engineers and Scientists; Fifth Edition is a proven text reference that provides a superior introduction to applied probability and statistics for engineering or science majors. The book lays emphasis in the manner in which probability yields insight into statistical problems; ultimately resulting in an intuitive understanding of the statistical procedures most often used by practicing engineers and scientists.

Real data from actual studies across life science; engineering; computing and business are incorporated in a wide variety of exercises and examples throughout the text. These examples and exercises are combined with updated problem sets and applications to connect probability theory to everyday statistical problems and situations. The book also contains end of chapter review material that highlights key ideas as well as the risks associated with practical application of the material. Furthermore; there are new additions to proofs in the estimation section as well as new coverage of Pareto and lognormal distributions; prediction intervals; use of dummy variables in multiple regression models; and testing equality of multiple population distributions.

This text is intended for upper level undergraduate and graduate students taking a course in probability and statistics for science or engineering; and for scientists; engineers; and other professionals seeking a reference of foundational content and application to these fields.

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  • Dedication
    Organization and Coverage
    Supplemental Materials

    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

    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

    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

    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

    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

    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

    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

    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

    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

    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

    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

    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

    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

    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

    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

    Appendix of Tables
  • Citation

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