Probability & Statistics for Engineers & Scientists + MyLab Statistic with Pearson eText, Global Edition

Series
Pearson
Author
Ronald E. Walpole / Raymond H. Myers / Sharon L. Myers / Keying E. Ye  
Publisher
Pearson
Cover
Softcover
Edition
9
Language
English
Pub.-date
January 2021
ISBN13
9781292161440
ISBN
1292161442
Related Titles


Product detail

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9781292161440
Probability & Statistics for Engineers & Scientists + MyLab Statistic with Pearson eText, Global Edition
106.60 approx. 7-9 days

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Description

For junior/senior undergraduates taking probability and statistics as applied to engineering, science, or computer science.
This package includes MyStatLab®.

 

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 package includes MyStatLab, an online homework, tutorial, and assessment program designed to work with this text to engage students and improve results. Within its structured environment, students practice what they learn, test their understanding, and pursue a personalized study plan that helps them absorb course material and understand difficult concepts.

 

MyStatLab should only be purchased when required by an instructor. Please be sure you have the correct ISBN and Course ID. Instructors, contact your Pearson representative for more information.

Features

This title is a Pearson Global Edition. The Editorial team at Pearson has worked closely with educators around the world to include content which is especially relevant to students outside the United States.

 

About the Book

  • 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 1115: linear regression; analysis of variance

This package includes MyStatLab, an online homework, tutorial, and assessment program designed to work with this text to engage students and improve results. Within its structured environment, students practice what they learn, test their understanding, and pursue a personalized study plan that helps them absorb course material and understand difficult concepts.

New to this Edition

New to the Ninth 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.

This package includes MyStatLab, an online homework, tutorial, and assessment program designed to work with this text to engage students and improve results. Within its structured environment, students practice what they learn, test their understanding, and pursue a personalized study plan that helps them absorb course material and understand difficult concepts.

Table of Contents

Preface

1. Introduction to Statistics and Data Analysis

1.1 Overview: Statistical Inference, Samples, Populations, and the Role of Probability

1.2 Sampling Procedures; Collection of Data

1.3 Measures of Location: The Sample Mean and Median

  Exercises

1.4 Measures of Variability

  Exercises

1.5 Discrete and Continuous Data

1.6 Statistical Modeling, Scientific Inspection, and Graphical Methods 19

1.7 General Types of Statistical Studies: Designed Experiment,

Observational Study, and Retrospective Study

  Exercises

2. Probability

2.1 Sample Space

2.2 Events

  Exercises

2.3 Counting Sample Points

  Exercises

2.4 Probability of an Event

2.5 Additive Rules

  Exercises

2.6 Conditional Probability, Independence and Product Rules

  Exercises

2.7 Bayes’ Rule

  Exercises

  Review Exercises

2.8 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters

3. Random Variables and Probability Distributions

3.1 Concept of a Random Variable

3.2 Discrete Probability Distributions

3.3 Continuous Probability Distributions

  Exercises

3.4 Joint Probability Distributions

  Exercises

  Review Exercises

3.5 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters

4. Mathematical Expectation

4.1 Mean of a Random Variable

  Exercises

4.2 Variance and Covariance of Random Variables

  Exercises

4.3 Means and Variances of Linear Combinations of Random Variables 127

4.4 Chebyshev’s Theorem

  Exercises

  Review Exercises

4.5 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters

5. Some Discrete Probability Distributions

5.1 Introduction and Motivation

5.2 Binomial and Multinomial Distributions

  Exercises

5.3 Hypergeometric Distribution

  Exercises

5.4 Negative Binomial and Geometric Distributions

5.5 Poisson Distribution and the Poisson Process

  Exercises

  Review Exercises

5.6 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters

6. Some Continuous Probability Distributions

6.1 Continuous Uniform Distribution

6.2 Normal Distribution

6.3 Areas under the Normal Curve

6.4 Applications of the Normal Distribution

  Exercises

6.5 Normal Approximation to the Binomial

  Exercises

6.6 Gamma and Exponential Distributions

6.7 Chi-Squared Distribution

6.8 Beta Distribution

6.9 Lognormal Distribution (Optional)

6.10 Weibull Distribution (Optional)

  Exercises

  Review Exercises

6.11 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters

7. Functions of Random Variables (Optional)

7.1 Introduction

7.2 Transformations of Variables

7.3 Moments and Moment-Generating Functions

  Exercises

8. Sampling Distributions and More Graphical Tools

8.1 Random Sampling and Sampling Distributions

8.2 Some Important Statistics

  Exercises

8.3 Sampling Distributions

8.4 Sampling Distribution of Means and the Central Limit Theorem

  Exercises

8.5 Sampling Distribution of S2

8.6 t-Distribution

8.7 F-Distribution

8.8 Quantile and Probability Plots

  Exercises

  Review Exercises

8.9 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters

9. One- and Two-Sample Estimation Problems

9.1 Introduction

9.2 Statistical Inference

9.3 Classical Methods of Estimation

9.4 Single Sample: Estimating the Mean

9.5 Standard Error of a Point Estimate

9.6 Prediction Intervals

9.7 Tolerance Limits

  Exercises

9.8 Two Samples: Estimating the Difference Between Two Means

9.9 Paired Observations

  Exercises

9.10 Single Sample: Estimating a Proportion

9.11 Two Samples: Estimating the Difference between Two Proportions

  Exercises

9.12 Single Sample: Estimating the Variance

9.13 Two Samples: Estimating the Ratio of Two Variances

  Exercises

9.14 Maximum Likelihood Estimation (Optional)

  Exercises

  Review Exercises

9.15 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters

10. One- and Two-Sample Tests of Hypotheses

10.1 Statistical Hypotheses: General Concepts

10.2 Testing a Statistical Hypothesis

10.3 The Use of P-Values for Decision Making in Testing Hypotheses

  Exercises

10.4 Single Sample: Tests Concerning a Single Mean

10.5 Two Samples: Tests on Two Means

10.6 Choice of Sample Size for Testing Means

10.7 Graphical Methods for Comparing Means

  Exercises

10.8 One Sample: Test on a Single Proportion

10.9 Two Samples: Tests on Two Proportions

  Exercises

10.10 One- and Two-Sample Tests Concerning Variances

  Exercises

10.11 Goodness-of-Fit Test

10.12 Test for Independence (Categorical Data)

10.13 Test for Homogeneity

10.14 Two-Sample Case Study

  Exercises

  Review Exercises

10.15 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters

11. Simple Linear Regression and Correlation

11.1 Introduction to Linear Regression

11.2 The Simple Linear Regression Model

11.3 Least Squares and the Fitted Model

  Exercises

11.4 Properties of the Least Squares Estimators

11.5 Inferences Concerning the Regression Coefficients

11.6 Prediction

  Exercises

11.7 Choice of a Regression Model

11.8 Analysis-of-Variance Approach

11.9 Test for Linearity of Regression: Data with Repeated Observations 416

  Exercises

11.10 Data Plots and Transformations

11.11 Simple Linear Regression Case Study

11.12 Correlation

  Exercises

  Review Exercises

11.13 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters

12. Multiple Linear Regression and Certain Nonlinear Regression Models

12.1 Introduction

12.2 Estimating the Coefficients

12.3 Linear Regression Model Using Matrices

  Exercises

12.4 Properties of the Least Squares Estimators

12.5 Inferences in Multiple Linear Regression

  Exercises

12.6 Choice of a Fitted Model through Hypothesis Testing

12.7 Special Case of Orthogonality (Optional)

  Exercises

12.8 Categorical or Indicator Variables

  Exercises

12.9 Sequential Methods for Model Selection

12.10 Study of Residuals and Violation of Assumptions

12.11 Cross Validation, Cp, and Other Criteria for Model Selection

  Exercises

12.12 Special Nonlinear Models for Nonideal Conditions

  Exercises

  Review Exercises

12.13 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters

13. One-Factor Experiments: General

13.1 Analysis-of-Variance Technique

13.2 The Strategy of Experimental Design

13.3 One-Way Analysis of Variance: Completely Randomized Design (One-Way ANOVA)

13.4 Tests for the Equality of Several Variances

  Exercises

13.5 Multiple Comparisons

  Exercises

13.6 Comparing a Set of Treatments in Blocks

13.7 Randomized Complete Block Designs

13.8 Graphical Methods and Model Checking

13.9 Data Transformations In Analysis of Variance)

  Exercises

13.10 Random Effects Models

13.11 Case Study

  Exercises

  Review Exercises

13.12 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters

14. Factorial Experiments (Two or More Factors)

14.1 Introduction

14.2 Interaction in the Two-Factor Experiment

14.3 Two-Factor Analysis of Variance

  Exercises

14.4 Three-Factor Experiments

  Exercises

14.5 Factorial Experiments for Random Effects and Mixed Models

  Exercises

  Review Exercises

14.6 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters

15. 2k Factorial Experiments and Fractions

15.1 Introduction

15.2 The 2k Factorial: Calculation of Effects and Analysis of Variance 598

15.3 Nonreplicated 2k Factorial Experiment

  Exercises

15.4 Factorial Experiments in a Regression Setting

15.5 The Orthogonal Design

  Exercises

15.6 Fractional Factorial Experiments

15.7 Analysis of Fractional Factorial Experiments

  Exercises

15.8 Higher Fractions and Screening Designs

15.9 Construction of Resolution III and IV Designs

15.10 Other Two-Level Resolution III Designs; The Plackett-Burman Designs

15.11 Introduction to Response Surface Methodology

15.12 Robust Parameter Design

  Exercises

  Review Exercises

15.13 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters

16. Nonparametric Statistics

16.1 Nonparametric Tests

16.2 Signed-Rank Test

  Exercises

16.3 Wilcoxon Rank-Sum Test

16.4 Kruskal-Wallis Test

  Exercises

16.5 Runs Test

16.6 Tolerance Limits

16.7 Rank Correlation Coefficient

  Exercises

  Review Exercises

17. Statistical Quality Control

17.1 Introduction

17.2 Nature of the Control Limits

17.3 Purposes of the Control Chart

17.4 Control Charts for Variables

17.5 Control Charts for Attributes

17.6 Cusum Control Charts

  Review Exercises

18 Bayesian Statistics

18.1 Bayesian Concepts

18.2 Bayesian Inferences

18.3 Bayes Estimates Using Decision Theory Framework

  Exercises

Bibliography

A. Statistical Tables and Proofs

B. Answers to Odd-Numbered Non-Review Exercises

Index