Probability and Statistics For Engineers & Scientists

Rs. 6,355
  • Author: Ronald E. Walpole
  • ISBN: 9789673498406
  • Publisher: Pearson Education
  • Edition: 9th
  • Format: Paperback – 856 pages
  • Language: English

SKU 9789673498406 Category Tags ,

Share
Description

For junior/senior undergraduates taking probability and statistics as applied to engineering, science, or computer science.

This classic text provides a rigorous introduction to basic probability theory and statistical inference, with a unique balance between theory and methodology. Interesting, relevant applications use real data from actual studies, showing how the concepts and methods can be used to solve problems in the field. This revision focuses on improved clarity and deeper understanding.

This latest edition is also available in as an enhanced Pearson eText. This exciting new version features an embedded version of StatCrunch, allowing students to analyze data sets while reading the book.

Key Features
  • The balance between theory and applications offers mathematical support to enhance coverage when necessary, giving engineers and scientists the proper mathematical context for statistical tools and methods.
  • Mathematical level: this text assumes one semester of differential and integral calculus as a prerequisite.
    • Calculus is confined to elementary probability theory and probability distributions (Chapters 2—7).
    • Matrix algebra is used modestly in coverage of linear regression material (Chapters 11—12).
    • Linear algebra and the use of matrices are applied in Chapters 11—15, where treatment of linear regression and analysis of variance is covered.
  • Compelling exercise sets challenge students to use the concepts to solve problems that occur in many real-life scientific and engineering situations. Many exercises contain real data from studies in the fields of biomedical, bioengineering, business, computing, etc.
    • Real-life applications of the Poisson, binomial, and hypergeometric distributions generate student interest using topics such as flaws in manufactured copper wire, highway potholes, hospital patient traffic, airport luggage screening, and homeland security.
  • Statistical software coverage in the following case studies includes SAS® and MINITAB®, with screenshots and graphics as appropriate:
    • Two-sample hypothesis testing
    • Multiple linear regression
    • Analysis of variance
    • Use of two-level factorial-experiments
  • Interaction plots provide examples of scientific interpretations and new exercises using graphics.
  • Topic outline
    • Chapter 1: elementary overview of statistical inference
    • Chapters 2—4: basic probability; discrete and continuous random variables
    • Chapters 2—10: probability distributions and statistical inferences
    • Chapters 5—6: specific discrete and continuous distributions with illustrations of their use and relationships among them
    • Chapter 7: optional chapter covering the transformation of random variables.
    • Chapter 8: additional materials on graphical methods; an important introduction to the notion of sampling distribution
    • Chapters 9—10: one and two sample point and interval estimation
    • Chapters 11—15: linear regression; analysis of variance
New to this Edition
  • Revised text focuses on improved clarity and deeper understanding rather than adding extraneous new material.
  • End-of-chapter material strengthens the connections between chapters.
    • “Pot Holes” comments remind students of the bigger picture and how each chapter fits into that picture. These notes also discuss limitations of specific procedures and help students avoid pitfalls in misusing statistics.
  • Class projects in several chapters provide the opportunity for students to gather their own experimental data and draw inferences from that data. These projects illustrate the meaning of a concept or provide empirical understanding of important statistical results, and are suitable for either group or individual work.
  • Case studies provide deeper insight into the practicality of the concepts.
Table of Contents
  1. Preface
  2. Introduction to Statistics and Data Analysis
    1. Overview: Statistical Inference, Samples, Populations, and the Role of Probability
    2. Sampling Procedures; Collection of Data
    3. Measures of Location: The Sample Mean and Median
    4. Exercises
    5. Measures of Variability
    6. Exercises
    7. Discrete and Continuous Data
    8. Statistical Modeling, Scientific Inspection, and Graphical Methods 19
    9. General Types of Statistical Studies: Designed Experiment,
    10. Observational Study, and Retrospective Study
    11. Exercises
  3. Probability
    1. Sample Space
    2. Events
    3. Exercises
    4. Counting Sample Points
    5. Exercises
    6. Probability of an Event
    7. Additive Rules
    8. Exercises
    9. Conditional Probability, Independence and Product Rules
    10. Exercises
    11. Bayes’ Rule
    12. Exercises
    13. Review Exercises
    14. Potential Misconceptions and Hazards; Relationship to Material in Other Chapters
  4. Random Variables and Probability Distributions
    1. Concept of a Random Variable
    2. Discrete Probability Distributions
    3. Continuous Probability Distributions
    4. Exercises
    5. Joint Probability Distributions
    6. Exercises
    7. Review Exercises
    8. Potential Misconceptions and Hazards; Relationship to Material in Other Chapters
  5. Mathematical Expectation
    1. Mean of a Random Variable
    2. Exercises
    3. Variance and Covariance of Random Variables
    4. Exercises
    5. Means and Variances of Linear Combinations of Random Variables 127
    6. Chebyshev’s Theorem
    7. Exercises
    8. Review Exercises
    9. Potential Misconceptions and Hazards; Relationship to Material in Other Chapters
  6. Some Discrete Probability Distributions
    1. Introduction and Motivation
    2. Binomial and Multinomial Distributions
    3. Exercises
    4. Hypergeometric Distribution
    5. Exercises
    6. Negative Binomial and Geometric Distributions
    7. Poisson Distribution and the Poisson Process
    8. Exercises
    9. Review Exercises
    10. Potential Misconceptions and Hazards; Relationship to Material in Other Chapters
  7. Some Continuous Probability Distributions
    1. Continuous Uniform Distribution
    2. Normal Distribution
    3. Areas under the Normal Curve
    4. Applications of the Normal Distribution
    5. Exercises
    6. Normal Approximation to the Binomial
    7. Exercises
    8. Gamma and Exponential Distributions
    9. Chi-Squared Distribution
    10. Beta Distribution
    11. Lognormal Distribution (Optional)
    12. Weibull Distribution (Optional)
    13. Exercises
    14. Review Exercises
    15. Potential Misconceptions and Hazards; Relationship to Material in Other Chapters
  8. Functions of Random Variables (Optional)
    1. Introduction
    2. Transformations of Variables
    3. Moments and Moment-Generating Functions
    4. Exercises
  9. Sampling Distributions and More Graphical Tools
    1. Random Sampling and Sampling Distributions
    2. Some Important Statistics
    3. Exercises
    4. Sampling Distributions
    5. Sampling Distribution of Means and the Central Limit Theorem
    6. Exercises
    7. Sampling Distribution of S2
    8. t-Distribution
    9. F-Distribution
    10. Quantile and Probability Plots
    11. Exercises
    12. Review Exercises
    13. Potential Misconceptions and Hazards; Relationship to Material in Other Chapters
  10. One- and Two-Sample Estimation Problems
    1. Introduction
    2. Statistical Inference
    3. Classical Methods of Estimation
    4. Single Sample: Estimating the Mean
    5. Standard Error of a Point Estimate
    6. Prediction Intervals
    7. Tolerance Limits
    8. Exercises
    9. Two Samples: Estimating the Difference Between Two Means
    10. Paired Observations
    11. Exercises
    12. Single Sample: Estimating a Proportion
    13. Two Samples: Estimating the Difference between Two Proportions
    14. Exercises
    15. Single Sample: Estimating the Variance
    16. Two Samples: Estimating the Ratio of Two Variances
    17. Exercises
    18. Maximum Likelihood Estimation (Optional)
    19. Exercises
    20. Review Exercises
    21. Potential Misconceptions and Hazards; Relationship to Material in Other Chapters
  11. One- and Two-Sample Tests of Hypotheses
    1. Statistical Hypotheses: General Concepts
    2. Testing a Statistical Hypothesis
    3. The Use of P-Values for Decision Making in Testing Hypotheses
    4. Exercises
    5. Single Sample: Tests Concerning a Single Mean
    6. Two Samples: Tests on Two Means
    7. Choice of Sample Size for Testing Means
    8. Graphical Methods for Comparing Means
    9. Exercises
    10. One Sample: Test on a Single Proportion
    11. Two Samples: Tests on Two Proportions
    12. Exercises
    13. One- and Two-Sample Tests Concerning Variances
    14. Exercises
    15. Goodness-of-Fit Test
    16. Test for Independence (Categorical Data)
    17. Test for Homogeneity
    18. Two-Sample Case Study
    19. Exercises
    20. Review Exercises
    21. Potential Misconceptions and Hazards; Relationship to Material in Other Chapters
  12. Simple Linear Regression and Correlation
    1. Introduction to Linear Regression
    2. The Simple Linear Regression Model
    3. Least Squares and the Fitted Model
    4. Exercises
    5. Properties of the Least Squares Estimators
    6. Inferences Concerning the Regression Coefficients
    7. Prediction
    8. Exercises
    9. Choice of a Regression Model
    10. Analysis-of-Variance Approach
    11. Test for Linearity of Regression: Data with Repeated Observations 416
    12. Exercises
    13. Data Plots and Transformations
    14. Simple Linear Regression Case Study
    15. Correlation
    16. Exercises
    17. Review Exercises
    18. Potential Misconceptions and Hazards; Relationship to Material in Other Chapters
  13. Multiple Linear Regression and Certain Nonlinear Regression Models
    1. Introduction
    2. Estimating the Coefficients
    3. Linear Regression Model Using Matrices
    4. Exercises
    5. Properties of the Least Squares Estimators
    6. Inferences in Multiple Linear Regression
    7. Exercises
    8. Choice of a Fitted Model through Hypothesis Testing
    9. Special Case of Orthogonality (Optional)
    10. Exercises
    11. Categorical or Indicator Variables
    12. Exercises
    13. Sequential Methods for Model Selection
    14. Study of Residuals and Violation of Assumptions
    15. Cross Validation, Cp, and Other Criteria for Model Selection
    16. Exercises
    17. Special Nonlinear Models for Nonideal Conditions
    18. Exercises
    19. Review Exercises
    20. Potential Misconceptions and Hazards; Relationship to Material in Other Chapters
  14. One-Factor Experiments: General
    1. Analysis-of-Variance Technique
    2. The Strategy of Experimental Design
    3. One-Way Analysis of Variance: Completely Randomized Design (One-Way ANOVA)
    4. Tests for the Equality of Several Variances
    5. Exercises
    6. Multiple Comparisons
    7. Exercises
    8. Comparing a Set of Treatments in Blocks
    9. Randomized Complete Block Designs
    10. Graphical Methods and Model Checking
    11. Data Transformations In Analysis of Variance)
    12. Exercises
    13. Random Effects Models
    14. Case Study
    15. Exercises
    16. Review Exercises
    17. Potential Misconceptions and Hazards; Relationship to Material in Other Chapters
  15. Factorial Experiments (Two or More Factors)
    1. Introduction
    2. Interaction in the Two-Factor Experiment
    3. Two-Factor Analysis of Variance
    4. Exercises
    5. Three-Factor Experiments
    6. Exercises
    7. Factorial Experiments for Random Effects and Mixed Models
    8. Exercises
    9. Review Exercises
    10. Potential Misconceptions and Hazards; Relationship to Material in Other Chapters
  16. 2k Factorial Experiments and Fractions
    1. Introduction
    2. The 2k Factorial: Calculation of Effects and Analysis of Variance 598
    3. Nonreplicated 2k Factorial Experiment
    4. Exercises
    5. Factorial Experiments in a Regression Setting
    6. The Orthogonal Design
    7. Exercises
    8. Fractional Factorial Experiments
    9. Analysis of Fractional Factorial Experiments
    10. Exercises
    11. Higher Fractions and Screening Designs
    12. Construction of Resolution III and IV Designs
    13. Other Two-Level Resolution III Designs; The Plackett-Burman Designs
    14. Introduction to Response Surface Methodology
    15. Robust Parameter Design
    16. Exercises
    17. Review Exercises
    18. Potential Misconceptions and Hazards; Relationship to Material in Other Chapters
  17. Nonparametric Statistics
    1. Nonparametric Tests
    2. Signed-Rank Test
    3. Exercises
    4. Wilcoxon Rank-Sum Test
    5. Kruskal-Wallis Test
    6. Exercises
    7. Runs Test
    8. Tolerance Limits
    9. Rank Correlation Coefficient
    10. Exercises
    11. Review Exercises
  18. Statistical Quality Control
    1. Introduction
    2. Nature of the Control Limits
    3. Purposes of the Control Chart
    4. Control Charts for Variables
    5. Control Charts for Attributes
    6. Cusum Control Charts
    7. Review Exercises
    8. Bayesian Statistics
    9. Bayesian Concepts
    10. Bayesian Inferences
    11. Bayes Estimates Using Decision Theory Framework
    12. Exercises
  19. Bibliography
  20. A. Statistical Tables and Proofs
  21. B. Answers to Odd-Numbered Non-Review Exercises
  22. Index

 

Additional information
Weight1.380 kg
Reviews

There are no reviews yet.

Only logged in customers who have purchased this product may leave a review.