STATISTICS%FOR BUSINESS AND ECONOMICSPDF电子书下载

1 COLLECTION AND PRESENTATION OF DATA 1

1.1 Introduction 2

1.2 Population and Sample Data 3

1.3 Sampling and Statistical Analysis 5

1.4 Data Sources 9

1.5 Experiments and Randomization 13

1.6 Other Types of Samples 14

1.7 Organizing，Condensing and Presenting Quantitative Data 17

Frequency Distributions 17

Histograms 21

Cumulative Frequency Distributions 21

1.8 Stem-and-Leaf Graphics （Optional） 26

1.9 Presenting Qualitative Data 31

2 DESCRIPTION AND SUMMARY OF DATA 43

2.1 Introduction 44

2.2 Descriptive Summary Measures 45

2.3 The Mean as a Measure of Central Tendency 46

2.4 The Weighted Mean 49

Approximation Method 49

Comments on Approximating a Mean 51

Exact Method 52

2.5 Applications of the Mean to Quality Control 53

2.6 The Median as a Measure of Central Tendency 57

Locating the Median in the Original Data Set 57

Locating the Median in Grouped Data 60

Algebraic Approximation of the Median 60

Graphical Approximation of the Median 61

2.7 The Mode as a Measure of Central Tendency 64

2.8 Percentiles，Deciles，and Quartiles 66

2.9 Skewness 68

2.10 Measures of Dispersion 68

2.11 The Range 72

2.12 The Interquartile Range and the Box Plot 75

2.13 Variance and Standard Deviations 77

2.14 The Variance and Standard Deviation in Frequency Form 82

2.15 Data Location and the Standard Deviation 86

2.16 Looking Ahead 89

3 PROBABILITY 105

3.1 Introduction 106

3.2 Relative Frequency and Probability 107

3.3 Experiments，Outcomes，and Probability 107

3.4 Events and Probability 110

3.5 Determining the Sample Space 111

3.6 Counting Techniques 113

3.7 Multiple Events 118

Unions and Intersections 119

Complements 119

Conditional Probability 121

3.8 Independence 123

3.9 Subjective Versus Objective Probability 133

3.10 Concluding Comments 137

4 RANDOM VARIABLES 143

4.1 Introduction 144

4.2 Random Variables and Probability 145

4.3 Probability Mass 147

4.4 The Expected Value of a Random Variable 148

4.5 The Dispersion of a Random Variable 152

4.6 Risk Assessment 154

Standard Deviation Comparison 154

Coefficient of Variation Comparison 155

4.7 Multiple Random Variables 158

4.8 Joint Probability Distributions 158

4.9 Additive Probability 163

4.10 Linear Functions of Random Variables 165

4.11 Expected Value and Variance of Sums of Random Variables 166

4.12 Covariance and Dependent Random Variables （Optional） 169

4.13 Concluding Comments 172

5 DISTRIBUTIONS OF DISCRETE RANDOM VARIABLES 179

5.1 Introduction 180

5.2 The Binomial Distribution 181

5.3 The Binomial Probability Mass Function 183

Determined with a Computer 185

Determined with a Binomial Table 186

5.4 Location and Dispersion 193

5.5 The Hypergeometric Distribution 200

5.6 The Poisson Distribution 204

5.7 Concluding Comments 211

6 DISTRIBUTIONS OF CONTINUOUS RANDOM VARIABLES 217

6.1 Introduction 218

6.2 Continuous Random Variables and Probability Distributions 219

6.3 Normal Distributions 226

6.4 The Standard Normal Distribution 228

6.5 Calculations with the Standard Normal Table 233

6.6 The Lognormal Distribution （Optional） 240

6.7 Concluding Comments 245

7 SAMPLING AND SAMPLING DISTRIBUTIONS 251

7.1 Introduction 252

7.2 Probability Samples 253

7.3 Simple Random Samples 253

Blind Draw 254

Random Number Table 255

Computerized Random Number Generator 256

7.4 Systematic Sampling 257

7.5 Stratified Sampling 259

7.6 Cluster Sampling 260

7.7 Sampling Distribution of the Sum of Random Variables 261

7.8 Sampling Distribution of the Sample Mean 265

7.9 Sampling Distribution of the Sample Mean for Large Samples 274

7.10 Sampling Distribution of the Sample Mean for Small Samples 278

7.11 Sampling Distribution of the Sample Proportion 281

7.12 Concluding Comments 284

Appendix 7 Avoiding Errors in Sampling 289

Validity 289

Reliability 290

Measurement 291

Omitted and Missing Observations 291

8 ESTIMATION 295

8.1 Introduction 296

8.2 Point Estimation 297

8.3 Properties of Estimators 298

8.4 Confidence Intervals 301

8.5 Confidence Interval for the Population Mean 302

8.6 Level of Confidence 305

8.7 When σ Is Unknown 307

8.8 Confidence Interval for the Mean，σ Unknown （and n small） 310

8.9 Estimating the Population Proportion π 315

8.10 Selecting the Sample Size 318

Selecting a Sample Size to Estimate μ 318

Selecting a Sample Size to Estimate π 320

8.11 Estimating the Population Median by Iteration （Optional） 322

8.12 Estimating the Population Median by the Bootstrap （Optional） 324

8.13 Concluding Comments 326

9 SINGLE SAMPLE HYPOTHESIS TESTING 335

9.1 Introduction 336

9.2 Hypotheses：An Illustration 337

Types and Cost of Errors 337

Stating the Hypotheses 339

9.3 Decision Rule：An Illustration 340

p-Value 340

Decision Rule 341

9.4 General Procedures for Hypothesis Testing 345

9.5 The Null and Alternative Hypotheses （Step 1） 345

One-Tailed Tests 347

Two-Tailed Tests 348

9.6 The Probabilities of Type I and Ⅱ Errors （Step 2） 349

One-Tailed Hypothesis Tests 351

Two-Tailed Hypothesis Tests 351

9.7 Selecting a Test Statistic （Step 3） 356

9.8 The Sample （Step 4） 358

9.9 Determining p-Values （Step 5） 358

Determining the p-Value for a One-Tailed t Test 359

Determining a p-Value for a Two-Tailed Test 362

9.10 Reaching a Conclusion （Step 6） 363

Using Confidence Intervals in Conclusions 364

Maintaining Uncertainty in Conclusions 364

9.11 Testing a Population Proportion 369

9.12 Test of the Median （Optional） 372

9.13 Bayesian Hypothesis Testing （Optional） 374

9.14 Concluding Comments 375

Appendix 9A The Type I and Type Ⅱ Error Tradeoff and the Effect of Sample Size 386

Appendix 9B The Operating-Characteristic Curve and the Power of a Test 389

Appendix 9C The Operating-Characteristic Curve and Acceptance Sampling 394

Appendix 9D Type I and Type Ⅱ Errors and Sample Size Selection 396

10 TWO-SAMPLE HYPOTHESIS TESTING 399

10.1 Introduction 400

10.2 Difference Between Means 401

When Variances Are Known （or Samples are Large） 402

When Variances Are Unknown but Assumed Equal 405

10.3 Test of Equality of Variances 410

One-Tailed Test 411

Two-Tailed Test 414

10.4 Small Samples of Different Size and Different Variances 417

10.5 Two-Sample Test without the Assumption of Normality 418

10.6 Matched Pairs 424

10.7 Testing Matched Pairs without the Assumption of Normality 430

10.8 Testing the Difference between Population Proportions 432

10.9 The Median Test for Two Samples （Optional） 436

10.10 Concluding Comments 438

11 ANALYSIS OF VARIANCE AND CONTINGENCY TABLES 447

11.1 Introduction 448

11.2 Variability Between and within Samples 448

Variability Between Samples 452

Variability within Samples 453

11.3 Comparing Critical and Calculated Values 454

11.4 The Analysis of Variance （ANOVA） Table 457

11.5 Two-Way ANOVA 464

11.6 Testing in Multiple Factor ANOVA 467

11.7 Multiple Factor ANOVA with a Computer 468

11.8 The Chi-Square Test for Independence 472

The Chi-Square Statistic 472

The p-value 475

11.9 Critical Values and the Chi-Square Distribution 475

11.10 Nonparametric ANOVA （Optional） 479

11.11 Looking Ahead 483

12 CORRELATION AND REGRESSION ANALYSIS WITHIN A SAMPLE 493

12.1 Introduction 494

12.2 Speed and Death：An Illustration 494

12.3 Conditional Means and Deviations 495

12.4 Deviations from the Means 498

12.5 Measures of Covariance 502

12.6 Correlation 504

12.7 The Regression Line 513

12.8 Errors in Predictions 516

12.9 The Method of Least Squares 517

12.10 Goodness-of-Fit and Correlation 524

12.11 Rank Correlation Measures （Optional） 530

12.12 Linear Regression and Correlation Via Computers 532

13 THE TWO-VARIABLE POPULATION MODEL 549

13.1 Introduction 550

13.2 The Phillips Curve 550

13.3 Deterministic Versus Stochastic Relationships 551

13.4 Estimation of Coefficients 555

13.5 The Confidence Interval for the Expected Value of y 559

13.6 The Prediction Interval for an Individual Value ofy 563

13.7 The Sampling Distribution of b 565

13.8 Testing Hypotheses About Individual Coefficients 567

13.9 Two-Tailed Test 568

13.10 Two-Tailed Test and Computer Output 570

13.11 Two-Tailed Test and Confidence Intervals 572

13.12 One-Tailed Test 575

13.13 Tests of Correlation （Optional） 578

13.14 Violations of the Assumptions 579

Normality 579

Lack of Linearity in a Hyperbolic Scatterplot （Optional） 581

Lack of Linearity in Scatterplots with Multiplicative Growth （Optional） 583

Some Other Data Transformations （Optional） 586

Nonconstant Error Term Variability （Optional） 594

Regressor Error Term Correlation （Optional） 595

13.15 Concluding Comments 601

14 MULTIPLE REGRESSION 615

14.1 Introduction 616

14.2 The Case of Equal Pay 616

14.3 Interpretation of Coefficients and the Prediction of y 618

14.4 Least Squares in Multiple Regression 623

14.5 Assumptions，Estimation，and Hypotheses Testing 626

14.6 Estimation of a Conditional Expected Value of y 633

14.7 Prediction of an Individual Value ofy 635

14.8 Hypotheses Testing 636

14.9 Confidence Interval for βj 642

14.10 Multiple Coefficient of Determination 644

14.11 Adjusted Coefficient of Determination 647

14.12 Testing the Population Model 648

14.13 Problems in Estimation 651

Insufficient Variability within an Explanatory Variable 651

Linear Relationship Among Explanatory Variables：Multicollinearity 652

14.14 Stepwise Regression （Optional） 656

14.15 Concluding Comments 662

15 TIME SERIES ANALYSIS AND FORECASTING 677

15.1 Introduction 678

15.2 Time Series Components 678

15.3 Trend Analysis 682

Forecasting with a Trend Line 682

Detrended Time Series 683

15.4 Seasonal Variation 686

15.5 Residual Analysis 695

15.6 Durbin-Watson Test 696

15.7 Runs Test 699

15.8 Dynamic Model 703

Building a Dynamic Model 703

Forecasting with a Dynamic Model 705

15.9 Testing for Autocorrelated Errors in Dynamic Models 707

15.10 Models Involving First Differences 708

15.11 Causal Model Building for Forecasting 712

15.12 Tradeoffs in Modeling 719

15.13 Concluding Comments 724

16 INDEX NUMBERS 739

16.1 Introduction 740

16.2 Measuring Price Change 740

16.3 Index Numbers 741

16.4 Other Index Weighting Schemes （Optional） 745

16.5 Chaining of Index Numbers 747

16.6 Splicing of Index Numbers 749

16.7 Seasonality and Smoothing of Index Numbers 754

Comparable Period Differencing 754

Moving Averages 754

Concluding Comments 760

17 DECISION ANALYSIS 767

17.1 Introduction 768

17.2 Decision Trees 768

17.3 Risk in Decision Making 772

17.4 Expected Utility Theory 772

17.5 An Application of Expected Utility to Production 775

17.6 Decision Analysis and New Sample Information 780

17.7 Computing Probabilities with Bayes’ Rule 784

17.8 Computing Probabilities with a Computer Spreadsheet 787

17.9 Optimal Decisions Based on Posterior Probabilities 788

17.10A Computer Based Decision-Making System 793

17.11 An Application of Expected Utility to the Value of Life （Optional） 796

17.12 Empirical Observations on Expected Utility Theory 799

18 STATISTICAL QUALITY CONTROL AND QUALITY MANAGEMENT 807

18.1 Introduction 808

18.2 A Perspective on Production and Quality Control 809

18.3 Statistical Quality Control and Graphical Tools 811

Flowcharts 812

Pareto Chart 814

Fishbone Diagram 816

Histogram 817

18.4 Statistical Process Control 817

18.5 Statistical Process Control：A Control Chart 818

18.6 Statistical Process Control：Examining Process Variability 823

18.7 Proportions and Their Application to Process Control 826

The c Chart 830

18.8 Acceptance Sampling by Attributes 833

18.9 Concluding Comments 835

Appendices 843

Short Answers to Selected Even-Numbered Exercises 901

Index 929