Probability and Statistics for Engineers and Scientists 9th edition by Ronald Walpole, Raymond Myers, Sharon Myers, Keying Ye 0134115856 9780134115856

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ISBN 10: 0134115856

ISBN 13: 9780134115856

Author: Ronald E. Walpole; Raymond H. Myers; Sharon L. Myers; Keying E. Ye

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.   Also available with MyStatLab MyStatLab™ is 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. Note: You are purchasing a standalone product; MyLab™ & Mastering™ does not come packaged with this content. Students, if interested in purchasing this title with MyLab & Mastering, ask your instructor for the correct package ISBN and Course ID. Instructors, contact your Pearson representative for more information. If you would like to purchase both the physical text and MyLab & Mastering, search for:   0134468910 / 9780134468914 Probability & Statistics for Engineers & Scientists, MyStatLab Update with MyStatLab plus Pearson eText — Access Card Package 9/e   Package consists of:    0134115856 / 9780134115856 Probability & Statistics for Engineers & Scientists, MyStatLab Update 0321847997 / 9780321847997 My StatLab Glue-in Access Card 032184839X / 9780321848390 MyStatLab Inside Sticker for Glue-In Packages

Probability and Statistics for Engineers and Scientists 9th Table of contents:

Chapter 1 Introduction to Statistics and Data Analysis

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

Use of Scientific Data

Variability in Scientific Data

The Role of Probability

How do Probability and Statistical Inference Work Together?

1.2 Sampling Procedures; Collection of Data

Simple Random Sampling

Experimental Design

Why Assign Experimental Units Randomly?

1.3 Measures of Location: The Sample Mean and Median

Other Measures of Locations

Exercises

1.4 Measures of Variability

Sample Range and Sample Standard Deviation

Units for Standard Deviation and Variance

Which Variability Measure is More Important?

Exercises

1.5 Discrete and Continuous Data

What Kinds of Problems are Solved in Binary Data Situations?

1.6 Statistical Modeling, Scientific Inspection, and Graphical Diagnostics

Scatter Plot

Stem-and-Leaf Plot

Histogram

Box-and-Whisker Plot or Box Plot

Other Distinguishing Features of a Sample

1.7 General Types of Statistical Studies: Designed Experiment, Observational Study, and Retrospective Study

What is Interaction?

What if Factors are Not Controlled?

Exercises

Chapter 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 the Product Rule

Conditional Probability

Independent Events

The Product Rule, or the Multiplicative Rule

Exercises

2.7 Bayes’ Rule

Total Probability

Bayes’ Rule

Exercises

Review Exercises

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

Chapter 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

Statistical Independence

What Are Important Characteristics of Probability Distributions and Where Do They Come From?

Exercises

Review Exercises

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

Chapter 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

What If the Function Is Nonlinear?

4.4 Chebyshev’s Theorem

Exercises

Review Exercises

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

Chapter 5 Some Discrete Probability Distributions

5.1 Introduction and Motivation

5.2 Binomial and Multinomial Distributions

The Bernoulli Process

Binomial Distribution

Where Does the Name Binomial Come From?

Areas of Application

Multinomial Experiments and the Multinomial Distribution

Exercises

5.3 Hypergeometric Distribution

Hypergeometric Distribution in Acceptance Sampling

Relationship to the Binomial Distribution

Exercises

5.4 Negative Binomial and Geometric Distributions

What Is the Negative Binomial Random Variable?

Applications of Negative Binomial and Geometric Distributions

5.5 Poisson Distribution and the Poisson Process

Properties of the Poisson Process

Nature of the Poisson Probability Function

Approximation of Binomial Distribution by a Poisson Distribution

Exercises

Review Exercises

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

Chapter 6 Some Continuous Probability Distributions

6.1 Continuous Uniform Distribution

6.2 Normal Distribution

6.3 Areas under the Normal Curve

Using the Normal Curve in Reverse

6.4 Applications of the Normal Distribution

Exercises

6.5 Normal Approximation to the Binomial

Exercises

6.6 Gamma and Exponential Distributions

Relationship to the Poisson Process

Applications of the Exponential and Gamma Distributions

The Memoryless Property and Its Effect on the Exponential Distribution

6.7 Chi-Squared Distribution

6.8 Beta Distribution

6.9 Lognormal Distribution

6.10 Weibull Distribution (Optional)

The Failure Rate for the Weibull Distribution

Interpretation of the Failure Rate

Exercises

Review Exercises

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

Chapter 7 Functions of Random Variables (Optional)

7.1 Introduction

7.2 Transformations of Variables

7.3 Moments and Moment-Generating Functions

Linear Combinations of Random Variables

Exercises

Chapter 8 Fundamental Sampling Distributions and Data Descriptions

8.1 Random Sampling

Populations and Samples

8.2 Some Important Statistics

Location Measures of a Sample: The Sample Mean, Median, and Mode

Variability Measures of a Sample: The Sample Variance, Standard Deviation, and Range

Exercises

8.3 Sampling Distributions

Inference about the Population from Sample Information

What Is the Sampling Distribution of Xˉ?

8.4 Sampling Distribution of Means and the Central Limit Theorem

The Central Limit Theorem

Inferences on the Population Mean

Sampling Distribution of the Difference between Two Means

What Do We Learn from Case Study 8.2?

More on Sampling Distribution of Means—Normal Approximation to the Binomial Distribution

Exercises

8.5 Sampling Distribution of S2

Degrees of Freedom as a Measure of Sample Information

8.6 t-Distribution

What Does the t-Distribution Look Like?

What Is the t-Distribution Used For?

8.7 F-Distribution

The F-Distribution with Two Sample Variances

What Is the F-Distribution Used For?

8.8 Quantile and Probability Plots

Quantile Plot

Normal Quantile-Quantile Plot

Normal Probability Plotting

Exercises

Review Exercises

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

Chapter 9 One- and Two-Sample Estimation Problems

9.1 Introduction

9.2 Statistical Inference

9.3 Classical Methods of Estimation

Unbiased Estimator

Variance of a Point Estimator

Interval Estimation

Interpretation of Interval Estimates

9.4 Single Sample: Estimating the Mean

One-Sided Confidence Bounds

The Case of σ Unknown

Concept of a Large-Sample Confidence Interval

9.5 Standard Error of a Point Estimate

9.6 Prediction Intervals

Use of Prediction Limits for Outlier Detection

9.7 Tolerance Limits

Distinction among Confidence Intervals, Prediction Intervals, and Tolerance Intervals

Exercises

9.8 Two Samples: Estimating the Difference between Two Means

The Experimental Conditions and the Experimental Unit

Variances Unknown but Equal

Interpretation of the Confidence Interval

Equal Sample Sizes

Unknown and Unequal Variances

9.9 Paired Observations

When Should Pairing Be Done?

Tradeoff between Reducing Variance and Losing Degrees of Freedom

Exercises

9.10 Single Sample: Estimating a Proportion

Choice of Sample Size

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)

The Likelihood Function

Additional Comments Concerning Maximum Likelihood Estimation

Exercises

Review Exercises

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

Chapter 10 One- and Two-Sample Tests of Hypotheses

10.1 Statistical Hypotheses: General Concepts

The Role of Probability in Hypothesis Testing

The Null and Alternative Hypotheses

10.2 Testing a Statistical Hypothesis

The Test Statistic

The Probability of a Type I Error

The Probability of a Type II Error

The Role of α, β, and Sample Size

Illustration with a Continuous Random Variable

One- and Two-Tailed Tests

How Are the Null and Alternative Hypotheses Chosen?

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

Preselection of a Significance Level

A Graphical Demonstration of a P-Value

How Does the Use of P-Values Differ from Classic Hypothesis Testing?

Exercises

10.4 Single Sample: Tests Concerning a Single Mean

Tests on a Single Mean (Variance Known)

Standardization of Xˉ

Relationship to Confidence Interval Estimation

Tests on a Single Sample (Variance Unknown)

Comment on the Single-Sample t-Test

Annotated Computer Printout for Single-Sample t-Test

10.5 Two Samples: Tests on Two Means

Unknown But Equal Variances

Unknown But Unequal Variances

Paired Observations

Problem of Interaction in a Paired t-Test

What Conditions Result in Interaction?

Annotated Computer Printout for Paired t-Test

Summary of Test Procedures

10.6 Choice of Sample Size for Testing Means

Two-Sample Case

10.7 Graphical Methods for Comparing Means

Annotated Computer Printout for Two-Sample t-Test

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

Robustness of χ2-Test to Assumption of Normality

F-Test for Testing Variances in SAS

Exercises

10.11 Goodness-of-Fit Test

10.12 Test for Independence (Categorical Data)

10.13 Test for Homogeneity

Testing for Several Proportions

10.14 Two-Sample Case Study

Statistical Significance and Engineering or Scientific Significance

Exercises

Review Exercises

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

Chapter 11 Simple Linear Regression and Correlation

11.1 Introduction to Linear Regression

11.2 The Simple Linear Regression (SLR) Model

The Fitted Regression Line

Another Look at the Model Assumptions

11.3 Least Squares and the Fitted Model

The Method of Least Squares

What Is Good about Least Squares?

Exercises

11.4 Properties of the Least Squares Estimators

Mean and Variance of Estimators

Partition of Total Variability and Estimation of σ2

The Estimator of σ2 as a Mean Squared Error

11.5 Inferences Concerning the Regression Coefficients

Hypothesis Testing on the Slope

Statistical Inference on the Intercept

A Measure of Quality of Fit: Coefficient of Determination

Pitfalls in the Use of R2

11.6 Prediction

Prediction Interval

Exercises

11.7 Choice of a Regression Model

11.8 Analysis-of-Variance Approach

Annotated Computer Printout for Simple Linear Regression

11.9 Test for Linearity of Regression: Data with Repeated Observations

Concept of Lack of Fit

What Is the Importance in Detecting Lack of Fit?

Annotated Computer Printout for Test for Lack of Fit

Exercises

11.10 Data Plots and Transformations

What Are the Implications of a Transformed Model?

Diagnostic Plots of Residuals: Graphical Detectionof Violation of Assumptions

Nonhomogeneous Variance

Normal Probability Plotting

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

Chapter 12 Multiple Linear Regression and Certain Nonlinear Regression Models

12.1 Introduction

12.2 Estimating the Coefficients

Polynomial Regression

12.3 Linear Regression Model Using Matrices

Exercises

12.4 Properties of the Least Squares Estimators

Analysis of Variance in Multiple Regression

12.5 Inferences in Multiple Linear Regression

Individual t-Tests for Variable Screening

Inferences on Mean Response and Prediction

Annotated Printout for Data of Example 12.4

More on Analysis of Variance in Multiple Regression (Optional)

Degrees of Freedom

Hypothesis of Interest

Exercises

12.6 Choice of a Fitted Model through Hypothesis Testing

The Adjusted Coefficient of Determination (Radj2)

How Are R2 and Radj2 Affected by Removal of x3?

Test on an Individual Coefficient

Does a Single Variable t-Test Have an F Counterpart?

Partial F-Tests on Subsets of Coefficients

12.7 Special Case of Orthogonality (Optional)

Exercises

12.8 Categorical or Indicator Variables

Model with Categorical Variables

Three Categories

Slope May Vary with Indicator Categories

Exercises

12.9 Sequential Methods for Model Selection

Illustration of Variable Screening in the Presence of Collinearity

Stepwise Regression

Summary

Choice of P-Values

12.10 Study of Residuals and Violation of Assumptions (Model Checking)

Illustration of Outlier Detection

Plotting Residuals for Case Study 12.1

Normality Checking

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

Use of the PRESS Statistic

The Cp Statistic

Exercises

12.12 Special Nonlinear Models for Nonideal Conditions

Nonhomogeneous Variance

Binary Response (Logistic Regression)

What Is the Model for Logistic Regression?

Characteristics of Logistic Function

Estimate of Effective Dose

Concept of Odds Ratio

Exercises

Review Exercises

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

Chapter 13 One-Factor Experiments: General

13.1 Analysis-of-Variance Technique

Two Sources of Variability in the Data

13.2 The Strategy of Experimental Design

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

Assumptions and Hypotheses in One-Way ANOVA

Model for One-Way ANOVA

Resolution of Total Variability into Components

Use of F-Test in ANOVA

F-Ratio for Testing Equality of Means

13.4 Tests for the Equality of Several Variances

Exercises

13.5 Single-Degree-of-Freedom Comparisons

13.6 Multiple Comparisons

Relationship between T and F

Confidence Interval Approach to a Paired Comparison

Experiment-wise Error Rate

Tukey’s Test

Where Does the α-Level Come From in Tukey’s Test?

Duncan’s Test

Dunnett’s Test: Comparing Treatment with a Control

Exercises

13.7 Comparing a Set of Treatments in Blocks

What Is the Purpose of Blocking?

13.8 Randomized Complete Block Designs

Model for the RCB Design

Further Comments Concerning Blocking

Interaction between Blocks and Treatments

13.9 Graphical Methods and Model Checking

What Is a Residual for an RCB Design?

13.10 Data Transformations in Analysis of Variance

Where Does Nonhomogeneous Variance Come From?

Exercises

13.11 Random Effects Models

Model and Assumptions for Random Effects Model

Estimation of Variance Components

Randomized Block Design with Random Blocks

13.12 Case Study

Residual Plots

Exercises

Review Exercises

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

Chapter 14 Factorial Experiments (Two or More Factors)

14.1 Introduction

Main Effects and Interaction

14.2 Interaction in the Two-Factor Experiment

Interaction and the Interpretation of Main Effects

A Graphical Look at Interaction

Need for Multiple Observations

14.3 Two-Factor Analysis of Variance

Model and Hypotheses for the Two-Factor Problem

Partitioning of Variability in the Two-Factor Case

Formation of Mean Squares

Impact of Significant Interaction in Example 14.1

Graphical Analysis for the Two-Factor Problem of Example 14.1

Exercises

14.4 Three-Factor Experiments

Impact of Interaction BC

Pooling in Multifactor Models

Factorial Experiments in Blocks

Exercises

14.5 Factorial Experiments for Random Effectsand Mixed Models

Mixed Model Experiment

Exercises

Review Exercises

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

Chapter 15 2k Factorial Experiments and Fractions

15.1 Introduction

Factor Screening and Sequential Experimentation

Screening Designs for Large Numbers of Factors

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

Computation of Sums of Squares

The 23 Factorial

Effects and Sums of Squares for the 2k

15.3 Nonreplicated 2k Factorial Experiment

Diagnostic Plotting with Nonreplicated 2k Factorial Experiments

Interpretation of Two-Factor Interaction

Analysis with Pooled Mean Square Error: Annotated Computer Printout

Exercises

15.4 Factorial Experiments in a Regression Setting

Interaction in the Regression Model

15.5 The Orthogonal Design

Standard Error of Coefficients and T-Tests

A More Thorough Look at the Orthogonality Property in the 2k Factorial

Center Runs with 2k Designs

Center Runs and Lack of Fit

An Intuitive Look at the Test on Curvature

Exercises

15.6 Fractional Factorial Experiments

Construction of 12 Fraction

Aliases in the 23−1

How Aliases Are Determined in General

Formal Construction of the 2k−1

Construction of the 14 Fraction

15.7 Analysis of Fractional Factorial Experiments

Exercises

15.8 Higher Fractions and Screening Designs

Design Resolution

15.9 Construction of Resolution III and IV Designs with 8, 16, and 32 Design Points

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

15.11 Introduction to Response Surface Methodology

The Second-Order Response Surface Model

Other Comments Concerning Response Surface Analysis

15.12 Robust Parameter Design

Control and Noise Variables

The Product Array

Noise Factors: Tolerances on Control Factors

Simultaneous Analysis of Process Mean and Variance

Alternative Approaches to Robust Parameter Design

The Role of the Control-by-Noise Interaction

Analysis Involving the Model Containing Both Control and Noise Variables

The Mean and Variance Response Surfaces

Exercises

Review Exercises

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

Chapter 16 Nonparametric Nonparametric Statistics

16.1 Nonparametric Tests

Sign Test

16.2 Signed-Rank Test

Two Samples with Paired Observations

Normal Approximation for Large Samples

Exercises

16.3 Wilcoxon Rank-Sum Test

Normal Theory Approximation for Two Samples

16.4 Kruskal-Wallis Test

Exercises

16.5 Runs Test

16.6 Tolerance Limits

16.7 Rank Correlation Coefficient

Exercises

Review Exercises

Chapter 17 Statistical Quality Control

17.1 Introduction

The Control Chart

17.2 Nature of the Control Limits

17.3 Purposes of the Control Chart

17.4 Control Charts for Variables

Rational Subgroups

Xˉ-Chart with Estimated Parameters

R-Charts to Control Variation

Xˉ- and R-Charts for Variables

Further Comments about Control Charts for Variables

Choice of Sample Size (Operating Characteristic Function) in the Case of the Xˉ-Chart

Choice of Sample Size for the R-Chart

Xˉ- and S-Charts for Variables

17.5 Control Charts for Attributes

The p-Chart for Fraction Defective

Choice of Sample Size for the p-Chart

Control Charts for Defects (Use of the Poisson Model)

17.6 Cusum Control Charts

Decision Rule for Cusum Charts

Review Exercises

Chapter 18 Bayes Bayesian Statistics

18.1 Bayesian Concepts

Subjective Probability

Conditional Perspective

Bayesian Applications

18.2 Bayesian Inferences

Point Estimation Using the Posterior Distribution

Bayesian Interval Estimation

18.3 Bayes Estimates Using Decision Theory Framework

Squared-Error Loss

Absolute-Error Loss

Exercises

Bibliography

Appendix A Statistical Tables and Proofs

A.24 Proof of Mean of the Hypergeometric Distribution

A.25 Proof of Mean and Variance of the Poisson Distribution

A.26 Proof of Mean and Variance of the Gamma Distribution

Appendix B Answers to Odd-Numbered Non-Review Exercises

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