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工程师和科学家实用统计分析  基于计算机的方法  英文版
工程师和科学家实用统计分析  基于计算机的方法  英文版

工程师和科学家实用统计分析 基于计算机的方法 英文版PDF电子书下载

社会科学

  • 电子书积分:13 积分如何计算积分?
  • 作 者:J.Wesiey Barnes著
  • 出 版 社:北京:清华大学出版社
  • 出版年份:2002
  • ISBN:7900641971
  • 页数:396 页
图书介绍:
《工程师和科学家实用统计分析 基于计算机的方法 英文版》目录

1 Introduction 1

2 Probability:Fundamental Concepts and Operational Rules 4

2.1 Repeatable Experiments and Sample Spaces 5

2.2 Events and the Venn Diagram 6

2.3 Probability and Operational Rules 9

2.4 Conditional Probability and Statistical Independence 13

List of Figures 15

CONTENTS 15

2.5 Bayes'Formula 16

2.6 Counting Techniques:Trees,Combinations,and Permutations 17

6.1 Introduction 18

List of Tables 19

Preface 23

Exercises 23

Computer-Based Exercises 27

3 Discrete Random Variables 28

3.1 Random Variables and General Properties of Probability Distributions 29

3.2 The Binomial Distribution 34

3.3 Some Popular Discrete Distributions and Their Relationships 39

3.4 A Suggestion for Solving Problems Involving Discrete Random Variables 50

3.5 Discrete Bivariate Probability Distribution Functions 50

Exercises 55

Computer-Based Exercises 60

4 Continuous Random Variables 62

4.1 General Properties of Continuous Random Variables 63

4.2 The Normal Distribution 65

4.3 Some Popular Continuous Distributions and Their Relationships 70

4.4 Continuous Bivariate Probability Density Functions 82

Exercises 86

Computer-Based Exercises 91

5 The Mean,Variance,Expected Value Operator,and Other Functions of Random Variables 93

5.1 Introduction 94

5.2 Measures of Centrality:The Mean,Median,and Mode 94

5.3 Measures of Variability:The Range and the Variance 98

5.4 The Expected Value Operator 100

5.5 Two Additional Measures of a Probability Distribution Function:Skewness and Kurtosis 101

5.6 The Covariance and Correlation of Bivariate Distribution Functions 107

5.7 Functions of One or More Random Variables 109

5.8 A Comment about the"Road Ahead" 113

Exercises 113

6 Classification and Description of Sample Data 118

6.2 The Frequency Table and Its Outgrowths 119

6.3 Graphical Presentations of the Frequency Table 122

6.4 Sample Estimates 126

6.5 Some Suggestions for Further Study 129

Exercises 130

7 Sampling Distributions:Random Sampling,the Sample Mean and Sample Variance,and the Central Limit Theorem 137

7.1 Random Sampling from Finite and Infinite Populations 138

7.2 The Distribution of the Sample Mean,? 142

7.3 The Distribution of the Sample Variance,S2 149

Exercises 151

8 Point and Interval Estimators and the Estimation of the Mean and the Variance 154

8.1 Point Estimators 155

8.2 Interval Estimators 157

8.3 Interval Estimates for Population Means 158

8.4 Determining an Adequate Sample Size for Interval Estimation of a Mean 163

8.5 Confidence Intervals for the Variance 163

8.6 Estimating Proportions 164

8.7 The Three General Types of Confidence Intervals 165

Exercises 167

9 Hypothesis Tests about a Single Mean,a Single Proportion,or a Single Variance 171

9.1 Introduction 172

9.2 Hypothesis Tests about a Single Mean 174

9.3 Hypothesis Tests about a Single Proportion 186

9.4 Hypothesis Tests about a Single Variance 188

9.5 Some Comments on the Difference between Statistical Significance and Practical Significance 191

Exercises 192

10 Hypothesis Tests for Two Means,Two Variances,or Two Proportions 195

10.1 Tests Comparing Two Means When σ1 and σ2 Are Known 196

10.2 Tests Comparing Two Means When σ1 and σ2 Are Unknown 199

10.3 Tests Comparing Two Means When the Data Are Paired 201

10.4 Tests Concerning Two Variances 202

10.5 Hypothesis Tests about Two Proportions 204

Exercises 207

11 Fitting Equations to Data,Part Ⅰ:Simple Linear Regression Analysis and Curvilinear Regression Analysis 211

11.1 Introduction 212

11.2 The Mathematical Model for Simple Linear Regression Analysis 213

11.3 Obtaining the Best Estimates of β0 and β1 215

11.4 The Multiple Correlation Coefficient Squared,r2 218

11.5 A Hypothesis Test for the Significance of the Fitted Line 220

11.6 The Construction of Confidence Intervals about β0,β1,the Mean of Y,and a Predicted Value of Y 222

11.7 The Correlation Coefficient and a Joint Confidence Region for β0 and β1 224

11.8 Graphical Methods of Investigating Data Structure in SLR 227

11.9 The Study of Sample Residuals in SLR 230

11.10 Curvilinear Regression 235

Exercises 240

12 Fitting Equations to Data,Part Ⅱ:Multivariate Regression Analysis 245

12.1 Introduction 246

12.2 Estimating the Parameter Values in MLR 248

12.3 Some Useful Theoretical Properties of MLR 250

12.4 The Variance-Covariance and Correlation Matrices of ? 252

12.5 Univariate Confidence Intervals on the βi and on predicted values of y 253

12.6 Determining Whether a Fit is Adequate and Comparing Competing Models 255

Exercises 259

12.7 The General Linear Model and"the Other Side of the Coin" 259

13 Hypothesis Tests for Two or More Means:Analysis of Variance-Single-Factor Designs 265

13.1 Introduction 266

13.2 Completely Randomized Single-Factor Experiments 266

13.3 How to Determine Which Means Differ When H0 Is Rejected 271

13 4 The Operating Characteristic Curve for the Completely Randomized Single-Factor Design 275

13.5 The Randomized Block Design:A Single-Factor Design with One Restriction on Randomization 280

13.6 Some Comments on Additional Single-Factor Designs and on Missing Data 285

Exercises 286

14 Factorial Analysis of Variance 293

14.1 Introduction 293

14.2 A Two-Factor Factorial ANOVA Design 294

14.3 Higher-Order Multifactor Factorial Designs 300

Exercises 300

15 An Introduction to Statistical Quality Control 303

15.2 A Control Chart for Variables:The?-R Chart 304

15.1 Introduction 304

15.3 Control Charts for Attributes 314

15.4 Acceptance Sampling:Construction of Sampling Plans and Their Uses 319

Exercises 327

16 Some Additional Methods of Data Analysis 332

16.1 Introduction 333

16.2 The x2 Test for Goodness of Fit 333

16.3 A Distribution-Free AIternative to the t Test:The Wilcoxon Signed Rank Test 336

16.4 A Distribution-Free Alternative to the Two-Sample t Test:The Wilcoxon Rank Sum Test 341

16.5 A Distribution-Free Alternative to the Completely Randomized Single-Factor ANOVA:The Kruskal-Wallis Test 344

Exercises 346

References and Suggested Readings 349

Statistcal Tables 351

Answers to Selected Exercises 375

Index 385

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