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Probability and Statistics for Engineers and Scientists, 9th edition

  • Sharon L. Myers
  • Ronald E. Walpole
  • Raymond H. Myers
  • Keying Ye
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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.


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Table of contents


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


1.4 Measures of Variability


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


2. Probability

2.1 Sample Space

2.2 Events


2.3 Counting Sample Points


2.4 Probability of an Event

2.5 Additive Rules


2.6 Conditional Probability, Independence and Product Rules


2.7 Bayes’ Rule


           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


3.4 Joint Probability Distributions


           Review Exercises

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

4. Mathematical Expectation

4.1 Mean of a Random Variable


4.2 Variance and Covariance of Random Variables


4.3 Means and Variances of Linear Combinations of Random Variables 127

4.4 Chebyshev’s Theorem


           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


5.3 Hypergeometric Distribution


5.4 Negative Binomial and Geometric Distributions

5.5 Poisson Distribution and the Poisson Process


           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


6.5 Normal Approximation to the Binomial


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)


           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


8. Sampling Distributions and More Graphical Tools

8.1 Random Sampling and Sampling Distributions

8.2 Some Important Statistics


8.3 Sampling Distributions

8.4 Sampling Distribution of Means and the Central Limit Theorem


8.5 Sampling Distribution of S2

8.6 t-Distribution

8.7 F-Distribution

8.8 Quantile and Probability Plots


           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


9.8 Two Samples: Estimating the Difference Between Two Means

9.9 Paired Observations


9.10 Single Sample: Estimating a Proportion

9.11 Two Samples: Estimating the Difference between Two Proportions


9.12 Single Sample: Estimating the Variance

9.13 Two Samples: Estimating the Ratio of Two Variances


9.14 Maximum Likelihood Estimation (Optional)


           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


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


10.8 One Sample: Test on a Single Proportion

10.9 Two Samples: Tests on Two Proportions


10.10 One- and Two-Sample Tests Concerning Variances


10.11 Goodness-of-Fit Test

10.12 Test for Independence (Categorical Data)

10.13 Test for Homogeneity

10.14 Two-Sample Case Study


           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


11.4 Properties of the Least Squares Estimators

11.5 Inferences Concerning the Regression Coefficients

11.6 Prediction


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


11.10 Data Plots and Transformations

11.11 Simple Linear Regression Case Study

11.12 Correlation


           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


12.4 Properties of the Least Squares Estimators

12.5 Inferences in Multiple Linear Regression


12.6 Choice of a Fitted Model through Hypothesis Testing

12.7 Special Case of Orthogonality (Optional)


12.8 Categorical or Indicator Variables


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


12.12 Special Nonlinear Models for Nonideal Conditions


           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


13.5 Multiple Comparisons


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)


13.10 Random Effects Models

13.11 Case Study


           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


14.4 Three-Factor Experiments


14.5 Factorial Experiments for Random Effects and Mixed Models


           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


15.4 Factorial Experiments in a Regression Setting

15.5 The Orthogonal Design


15.6 Fractional Factorial Experiments

15.7 Analysis of Fractional Factorial Experiments


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


           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


16.3 Wilcoxon Rank-Sum Test

16.4 Kruskal-Wallis Test


16.5 Runs Test

16.6 Tolerance Limits

16.7 Rank Correlation Coefficient


           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



A. Statistical Tables and Proofs

B. Answers to Odd-Numbered Non-Review Exercises


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Published by Pearson (March 10th 2016) - Copyright © 2017