INTRODUCTION TO PROBABILITY AND STATISTICS FOR ENGINEERS AND SCIENTISIS Fourth Edition2025|PDF|Epub|mobi|kindle电子书版本百度云盘下载

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Chapter Ⅰ Introduction to Statistics1

1.1 Introduction1

1.2 Data Collection and Descriptive Statistics1

1.3 Inferential Statistics and Probability Models2

1.4 Populations and Samples3

1.5 A Brief History of Statistics3

Problems7

Chapter 2 Descriptive Statistics9

2.1 Introduction9

2.2 Describing Data Sets9

2.2.1 Frequency Tables and Graphs10

2.2.2 Relative Frequency Tables and Graphs10

2.2.3 Grouped Data，Histograms，Ogives，and Stem and Leaf Plots14

2.3 Summarizing Data Sets17

2.3.1 Sample Mean，Sample Median，and Sample Mode17

2.3.2 Sample Variance and Sample Standard Deviation22

2.3.3 Sample Percentiles and Box Plots24

2.4 Chebyshev’s Inequality27

2.5 Normal Data Sets31

2.6 Paired Data Sets and the Sample Correlation Coefficient33

Problems41

Chapter 3 Elements of Probability55

3.1 Introduction55

3.2 Sample Space and Events56

3.3 Venn Diagrams and the Algebra of Events58

3.4 Axioms of Probability59

3.5 Sample Spaces Having Equally Likely Outcomes61

3.6 Conditional Probability67

3.7 Bayes’Formula70

3.8 Independent Events76

Problems80

Chapter 4 Random Variables and Expectation89

4.1 Random Variables89

4.2 Types of Random Variables92

4.3 Jointly Distributed Random Variables95

4.3.1 Independent Random Variables101

4.3.2 Conditional Distributions105

4.4 Expectation107

4.5 Properties of the Expected Value111

4.5.1 Expected Value of Sums of Random Variables115

4.6 Variance118

4.7 Covariance and Variance of Sums of Random Variables121

4.8 Moment Generating Functions125

4.9 Chebyshev’s Inequality and the Weak Law of Large Numbers127

Problems130

Chapter 5 Special Random Variables141

5.1 The Bernoulli and Binomial Random Variables141

5.1.1 Computing the Binomial Distribution Function147

5.2 The Poisson Random Variable148

5.2.1 Computing the Poisson Distribution Function155

5.3 The Hypergeometric Random Variable156

5.4 The Uniform Random Variable160

5.5 Normal Random Variables168

5.6 Exponential Random Variables176

5.6.1 The Poisson Process180

5.7 The Gamma Distribution183

5.8 Distributions Arising from the Normal186

5.8.1 The Chi-Square Distribution186

5.8.2 The t-Distribution190

5.8.3 The F-Distribution192

5.9 The Logistics Distribution193

Problems195

Chapter 6 Distributions of Sampling Statistics203

6.1 Introduction203

6.2 The Sample Mean204

6.3 The Central Limit Theorem206

6.3.1 Approximate Distribution of the Sample Mean212

6.3.2 How Large a Sample Is Needed？214

6.4 The Sample Variance215

6.5 Sampling Distributions from a Normal Population216

6.5.1 Distribution of the Sample Mean217

6.5.2 Joint Distribution of X and S2217

6.6 Sampling from a Finite Population219

Problems223

Chapter 7 Parameter Estimation231

7.1 Introduction231

7.2 Maximum Likelihood Estimators232

7.2.1 Estimating Life Distributions240

7.3 Interval Estimates242

7.3.1 Confidence Interval for a Normal Mean When the Variance Is Unknown248

7.3.2 Confidence Intervals for the Variance of a Normal Distribution253

7.4 Estimating the Difference in Means of Two Normal Populations255

7.5 Approximate Confidence Interval for the Mean of a Bernoulli Random Variable262

7.6 Confidence Interval of the Mean of the Exponential Distribution267

7.7 Evaluating a Point Estimator268

7.8 The Bayes Estimator274

Problems279

Chapter 8 Hypothesis Testing293

8.1 Introduction294

8.2 Significance Levels294

8.3 Tests Concerning the Mean of a Normal Population295

8.3.1 Case of Known Variance295

8.3.2 Case of Unknown Variance：The t-Test307

8.4 Testing the Equality of Means of Two Normal Populations314

8.4.1 Case of Known Variances314

8.4.2 Case of Unknown Variances316

8.4.3 Case of Unknown and Unequal Variances320

8.4.4 The Paired t-Test321

8.5 Hypothesis Tests Concerning the Variance of a Normal Population323

8.5.1 Testing for the Equality of Variances of Two Normal Populations324

8.6 Hypothesis Tests in Bernoulli Populations325

8.6.1 Testing the Equality of Parameters in Two Bernoulli Populations329

8.7 Tests Concerning the Mean of a Poisson Distribution332

8.7.1 Testing the Relationship Between Two Poisson Parameters333

Problems336

Chapter 9 Regression353

9.1 Introduction353

9.2 Least Squares Estimators of the Regression Parameters355

9.3 Distribution of the Estimators357

9.4 Statistical Inferences About the Regression Parameters363

9.4.1 Inferences Concerning β364

9.4.2 Inferences Concerning α372

9.4.3 Inferences Concerning the Mean Response α＋βx0373

9.4.4 Prediction Interval of a Future Response375

9.4.5 Summary of Distributional Results377

9.5 The Coefficient of Determination and the Sample Correlation Coefficient378

9.6 Analysis of Residuals：Assessing the Model380

9.7 Transforming to Linearity383

9.8 Weighted Least Squares386

9.9 Polynomial Regression393

9.10 Multiple Linear Regression396

9.10.1 Predicting Future Responses407

9.11 Logistic Regression Models for Binary Output Data412

Problems415

Chapter 10 Analysis of Variance441

10.1 Introduction441

10.2 An Overview442

10.3 One-Way Analysis of Variance444

10.3.1 Multiple Comparisons of Sample Means452

10.3.2 One-Way Analysis of Variance with Unequal Sample Sizes454

10.4 Two-Factor Analysis of Variance：Introduction and Parameter Estimation456

10.5 Two-Factor Analysis of Variance：Testing Hypotheses460

10.6 Two-Way Analysis of Variance with Interaction465

Problems473

Chapter Ⅱ Goodness of Fit Tests and Categorical Data Analysis485

11.1 Introduction485

11.2 Goodness of Fit Tests When All Parameters Are Specified486

11.2.1 Determining the Critical Region by Simulation492

11.3 Goodness of Fit Tests When Some Parameters Are Unspecified495

11.4 Tests of Independence in Contingency Tables497

11.5 Tests of Independence in Contingency Tables Having Fixed Marginal Totals501

11.6 The Kolmogorov-Smirnov Goodness of Fit Test for Continuous Data506

Problems510

Chapter 12 Nonparametric Hypothesis Tests517

12.1 Introduction517

12.2 The Sign Test517

12.3 The Signed Rank Test521

12.4 The Two-Sample Problem527

12.4.1 The Classical Approximation and Simulation531

12.5 The Runs Test for Randomness535

Problems539

Chapter 13 Quality Control547

13.1 Introduction547

13.2 Control Charts for Average Values：The X-Control Chart548

13.2.1 Case of Unknown μ and σ551

13.3 S-Control Charts556

13.4 Control Charts for the Fraction Defective559

13.5 Control Charts for Number of Defects561

13.6 Other Control Charts for Detecting Changes in the Population Mean565

13.6.1 Moving-Average Control Charts565

13.6.2 Exponentially Weighted Moving-Average Control Charts567

13.6.3 Cumulative Sum Control Charts573

Problems575

Chapter 14 Life Testing583

14.1 Introduction583

14.2 Hazard Rate Functions583

14.3 The Exponential Distribution in Life Testing586

14.3.1 Simultaneous Testing——Stopping at the rth Failure586

14.3.2 Sequential Testing592

14.3.3 Simultaneous Testing—— Stopping by a Fixed Time596

14.3.4 The Bayesian Approach598

14.4 A Two-Sample Problem600

14.5 The Weibull Distribution in Life Testing602

14.5.1 Parameter Estimation by Least Squares604

Problems606

Chapter 15 Simulation，Bootstrap Statistical Methods，and Permutation Tests613

15.1 Introduction613

15.2 Random Numbers614

15.2.1 The Monte Carlo Simulation Approach616

15.3 The Bootstrap Method617

15.4 Permutation Tests624

15.4.1 Normal Approximations in Permutation Tests627

15.4.2 Two-Sample Permutation Tests631

15.5 Generating Discrete Random Variables632

15.6 Generating Continuous Random Variables634

15.6.1 Generating a Normal Random Variable636

15.7 Determining the Number of Simulation Runs in a Monte Carlo Study637

Problems638

Appendix of Tables641

Index647