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Applied Statistics for Engineers and Scientists

by: Petruccelli, Joseph; Nandram, Balgobin; Chen, Minghui

Applied Statistics for Engineers and Scientists

by: Petruccelli, Joseph; Nandram, Balgobin; Chen, Minghui

ISBN 13:
9780135659533
ISBN 10:
0135659531
Edition: 1st
Format: Paperback
Copyright: 01/26/1999
Publisher: Pearson

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Summary

Designed for courses in Probability and Statistics and/or Random Processes, this book integrates a modern approach to statistical methods, computer technology, and recent pedagogical trends into an introductory curriculum for engineers and scientists.It reflects the most current topics and data available, including computer techniques, inference through bootstrapping, and process stability.

PREFACE

(6)

PREFACE--TO THE STUDENT

1 INTRODUCTION TO DATA ANALYSIS

(34)

Introduction

(1)

1.1 Statistics in Real Life

(1)

1.2 Variation in Data

(4)

1.3 Stationary Processes

(1)

1.4 Assessing Stationarity

(5)

1.5 Causes of Variation

(3)

1.6 Stratified Plots

(2)

1.7 Identifying Possible Causes of Variation

(3)

1.8 An Application: Gage R&R

(14)

2 SUMMARIZING DATA

(48)

Introduction

(1)

2.1 Variables and Data

(3)

2.2 Displaying Data Distributions

(11)

2.3 Summary Measures for Quantitative Data

(8)

2.4 Outliers

(6)

2.5 Resistance of Summary Measures

(2)

2.6 Trimmed and Winsorized Means

(1)

2.7 The Calculation and Use of Summary Measures: An Extended Illustration

(5)

2.8 Exploratory Data Analysis

(11)

3 DESIGNING STUDIES AND OBTAINING DATA

(47)

Introduction

(1)

3.1 The Role of Statistics in Producing and Analyzing Data

(1)

3.2 Selecting Sampling Units

(8)

3.3 Types of Designed Studies

(1)

3.4 Controlled Experiments

(16)

3.5 Observational Studies

110

(20)

4 AN INTRODUCTION TO STATISTICAL MODELING

130

(97)

Introduction

130

(2)

4.1 Density Histograms

132

(2)

4.2 The Notion of Probability

134

(10)

4.3 Discrete Random Variables and Their Distribution Models

144

(30)

4.4 The Power of Models

174

(1)

4.5 Continuous Distribution Models

174

(17)

4.6 The Central Limit Theorem

191

(8)

4.7 Identifying Common Distributions

199

(7)

4.8 Transformations to Normality

206

(21)

5 INTRODUCTION TO INFERENCE: ESTIMATION AND PREDICTION

227

(60)

Introduction

227

(1)

5.1 Inference: Some Preliminary Considerations

228

(2)

5.2 The Models

230

(5)

5.3 Types of Inference

235

(1)

5.4 Estimation for the C+E Model

235

(16)

5.5 Predicting a New Observation from the C+E Model

251

(2)

5.6 Estimation of a Population Proportion

253

(3)

5.7 Determining the Sample Size

256

(2)

5.8 Estimation for Two-Population C+E Data

258

(8)

5.9 Comparing Two Population Proportions

266

(2)

5.10 Classical Tolerance Intervals

268

(2)

5.11 Robustness

270

(17)

6 HYPOTHESIS TESTS

287

(65)

Introduction

287

(1)

6.1 Preliminaries

288

(1)

6.2 The Machinery of Hypothesis Testing

288

(9)

6.3 The Philosophy of Hypothesis Testing

297

(3)

6.4 Statistical Significance

300

(1)

6.5 Pitfalls of Hypothesis Testing

301

(3)

6.6 Hypothesis Tests for the Mean in the C+E Model

304

(7)

6.7 One-Sample Hypothesis Tests for a Population Proportion

311

(3)

6.8 Comparing Two-Population Means

314

(9)

6.9 Comparing Two-Population Proportions

323

(2)

6.10 Fixed-Significance-Level Tests

325

(2)

6.11 Power

327

(4)

6.12 The Relation Between Hypothesis Tests and Confidence Intervals

331

(21)

7 THE RELATIONSHIP BETWEEN TWO VARIABLES

352

(88)

Introduction

352

(1)

7.1 Displaying and Summarizing Association in Bivariate Quantitative Data

353

(9)

7.2 Correlation: Measuring Association in Bivariate Quantitative Data

362

(6)

7.3 Simple Linear Regression: Modeling Bivariate Quantitative Data

368

(35)

7.4 The Relationship between Two Categorical Variables

403

(16)

7.5 Cause and Effect

419

(1)

7.6 The Issue of Stationarity

420

(20)

8 MULTIPLE REGRESSION

440

(79)

Introduction

440

(1)

8.1 The Multiple Linear Regression Model

441

(2)

8.2 Interpreting the Response Surface

443

(5)

8.3 The Modeling Process

448

(1)

8.4 The Graphical Exploration of Multivariable Data

449

(9)

8.5 Fitting the Multiple Linear Regression Model

458

(3)

8.6 Assessing Model Fit

461

(5)

8.7 Interpretation of the Fitted Model

466

(1)

8.8 Theory-Based Modeling

467

(3)

8.9 Comparison of Fitted Models

470

(5)

8.10 Analysis of Variance

475

(1)

8.11 Inference for the Regression Model

476

(9)

8.12 Multicollinearity

485

(2)

8.13 More on Empirical Model Building

487

(5)

8.14 A Final Word on Regression Model Building

492

(27)

9 THE ONE-WAY MODEL

519

(55)

Introduction

519

(1)

9.1 The One-Way Means Model

520

(22)

9.2 The One-Way Effects Model

542

(4)

9.3 Blocking in the One-Way Model

546

(17)

9.4 The Advantage of Blocking

563

(11)

10 THE FACTORIAL MODEL

574

(65)

Introduction

574

(1)

10.1 The Factorial Means Model

575

(7)

10.2 Interactions

582

(5)

10.3 The Factorial Effects Model

587

(14)

10.4 Contrasts

601

(3)

10.5 What About Those Questionable Model Assumptions?

604

(2)

10.6 Transformations to Remove Interactions

606

(5)

10.7 Analysis of the Main Effects

611

(4)

10.8 Steps in Analyzing Factorial Models

615

(1)

10.9 Why Factorial Designs?

615

(1)

10.10 One Observation Per Cell

616

(1)

10.11 Regression and the Factorial Model

617

(5)

10.12 Unbalanced Designs

622

(9)

10.13 Multifactor Models

631

(8)

11 DISTRIBUTION-FREE INFERENCE

639

(84)

Introduction

639

(1)

11.1 Inference: Some Preliminary Considerations

640

(1)

11.2 The Sign Test

641

(6)

11.3 Estimation Based on the Sign Test

647

(2)

11.4 Rank-Based Methods I: One- and Two-Sample Location Problems

649

(14)

11.5 Rank-Based Methods II: The Relationship Between Two Variables

663

(7)

11.6 Rank-Based Methods III: The One-Way Model

670

(6)

11.7 Advantages and Disadvantages of Rank-Based Tests

676

(1)

11.8 Permutation Distributions: A Different Paradigm for Inference

676

(3)

11.9 Permutation Tests

679

(12)

11.10 Randomization Tests

691

(1)

11.11 The Bootstrap

692

(15)

11.12 A Distribution-Free Tolerance Interval

707

(16)

12 2(k) DESIGNS

723

(52)

Introduction

723

(1)

12.1 The Nature of Scientific Investigation

724

(1)

12.2 The Role of Statistics in Scientific Investigation

725

(1)

12.3 Notions and Terminology of Experimental Design

726

(1)

12.4 The OFAT Design

727

(1)

12.5 The Factorial Design

728

(5)

12.6 Designing and Running a 2(2) Experiment

733

(1)

12.7 A Model for 2(2) Experimental Data

734

(2)

12.8 Analyzing a 2(2) Experiment

736

(4)

12.9 The 2(2) Design with Center Points

740

(4)

12.10 The General 2(k) Design

744

(14)

12.11 Transformations

758

(1)

12.12 Residual Analysis

759

(2)

12.13 A Final Example

761

(14)

13 2(k-p) DESIGNS AND THEIR ROLE IN QUALITY IMPROVEMENT

775

(37)

Introduction

775

(1)

13.1 The Need for Parsimonious Experimentation

776

(1)

13.2 Confounding

776

(1)

13.3 Fractional Factorial Designs

777

(1)

13.4 Constructing Fractional Factorial Designs

778

(6)

13.5 Analyzing Fractional Factorial Designs

784

(5)

13.6 Blocking

789

(3)

13.7 The Strategy of Sequential Experimentation

792

(3)

13.8 Taguchi's Robust Parameter Design

795

(17)

14 RESPONSE-SURFACE METHODOLOGY

812

(30)

Introduction

812

(1)

14.1 Why Map the Response Surface?

813

(1)

14.2 Approximating the Response Surface

814

(1)

14.3 The Geometry of First-Order and Second-Order Surfaces

815

(7)

14.4 Heading for the Optimum: First Steps

822

(3)

14.5 Second-Order Designs

825

(5)

14.6 Summary of Steps in Mapping a Response Surface

830

(12)

15 STATISTICAL PROCESS CONTROL

842

(52)

Introduction

842

(1)

15.1 The New Quality Philosophy

843

(4)

15.2 Control Charts for Measurement Data

847

(23)

15.3 Control Charts for Attribute Data

870

(9)

15.4 Process Capability

879

(15)

APPENDIX A--TABLES

894

(30)

A.1 Binomial Probabilities

894

(5)

A.2 Poisson Probabilities

899

(2)

A.3 Areas Under the Standard Normal Density

901

(3)

A.4 Critical Values of the t Distribution

904

(2)

A.5 Critical Values of the Chi(2) Distribution

906

(2)

A.6 Critical Values of the F Distribution

908

(3)

A.7 Table of Random Digits

911

(2)

A.8 Table of Constants for Normal-Theory Tolerance Intervals

913

(1)

A.9 Studentized Range Distribution Quantities

914

(3)

A.10 Probabilities of the Spearman Correlation

917

(1)

A.11 Control Chart Constants

918

(1)

A.12 Lower Tail Probabilities for the Wilcoxon Signed Rank Statistic

919

(1)

A.13 Lower Tail Probabilities for the Wilcoxon Rank Sum Statistic

920

(1)

A.14 Critical Constant k for Wilcoxon Signed Rank Confidence Intervals

921

(1)

A.15 Critical Constant k for Wilcoxon Rank Sum Confidence Intervals

922

(2)

ANSWERS TO ODD-NUMBERED EXERCISES

924

(18)

INDEX

942

Amazon no longer offers textbook rentals. We do!

Applied Statistics for Engineers and Scientists

Applied Statistics for Engineers and Scientists

9780135659533

0135659531

Summary

Table of Contents

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