Introduction to executive protectionPDF电子书下载

PART Ⅰ: Data 1

CHAPTER1Looking at Data-Distributions 2

Introduction 4

Variables 4

1.1 Displaying Distributions with Graphs 5

Graphs for categorical variables 6

Measuring the speed of light 7

Measurement 8

Variation 9

Stemplots 10

Examining distributions 13

Histograms 14

Looking at data 17

Time plots 18

Beyond the basics: decomposing time series 21

Section 1.1 Exercises 23

1.2 Describing Distributions with Numbers 40

Measuring center: the mean 41

Measuring center: the median 42

Mean versus median 43

Measuring spread: the quartiles 44

Measuring spread: the interquartile range 46

The five-number summary and boxplots 47

Comparing distributions 50

Measuring spread: the standard deviation 51

Properties of the standard deviation 53

Choosing measures of center and spread 54

Changing the unit of measurement 55

Section 1.2 Exercises 58

1.3 The Normal Distributions 64

Density curves 66

Measuring center and spread for density curves 68

Normal distributions 70

The 68-95-99.7 rule 72

Standardizing observations 73

The standard normal distribution 74

Normal distribution calculations 76

Normal quantile plots 79

Beyond the basics: density estimation 84

Section 1.3 Exercises 85

Chapter 1 Exercises 93

CHAPTER 2Looking at Data-Relationships 102

Introduction 104

Getting started 104

2.1 Scatterplots 106

Interpreting scatterplots 107

Adding categorical variables to scatterplots 108

More examples of scatterplots 109

Beyond the basics: scatterplot smoothers 112

Categorical explanatory variables 115

Section 2.1 Exercises 117

2.2 Correlation 126

The correlation r 126

Properties of correlation 128

Section 2.2 Exercises 131

2.3 Least-Squares Regression 135

Fitting a line to data 137

Prediction 137

Least-squares regression 139

Interpreting the regression line 142

Correlation and regression 142

Understanding r 2 145

Section 2.3 Exercises 148

2.4 Cautions about Regression and Correlation 153

Residuals 154

Lurking variables 156

Outliers and inuential observations 160

Beyond the basics: regression diagnostics 163

Beware the lurking variable 166

Beware correlations based on averaged data 167

The restricted-range problem 168

Section 2.4 Exercises 169

2.5 An Application: Exponential Growth and WorldOil Production 181

The nature of exponential growth 181

The logarithm transformation 184

Residuals again 185

Prediction in the exponential growth model 188

Section 2.5 Exercises 189

2.6 Relations in Categorical Data 193

Marginal distributions 194

Describing relationships 196

Conditional distributions 196

Simpson's paradox 199

The perils of aggregation 200

Section 2.6 Exercises 201

2.7 The Question of Causation 207

Explaining association: causation 208

Explaining association: common response 209

Explaining association: confounding 209

Establishing causation 210

Section 2.7 Exercises 212

Chapter 2 Exercises 214

CHAPTER 3Producing Data 228

Introduction 230

3.1 First Steps 230

Where to find data: the libraryand the Internet 231

Sampling 233

Experiments 234

Section 3.1 Exercises 235

3.2 Design of Experiments 237

Comparative experiments 239

Randomization 241

Randomized comparative experiments 242

How to randomize 243

Cautions about experimentation 246

Matched pairs design 246

Block designs 247

Section 3.2 Exercises 250

3.3 Sampling Design 256

Simple random samples 257

Stratified samples 258

Multistage samples 259

Cautions about sample surveys 260

Section 3.3 Exercises 262

3.4 Toward Statistical Inference 267

Sampling variability 268

Sampling distributions 269

The bias of a statistic 272

The variability of a statistic 272

Bias and variability 274

Why randomize? 275

Beyond the basics: capture-recapture sampling 275

Section 3.4 Exercises 277

Chapter 3 Exercises 281

PART Ⅱ: Probability and Inference 287

CHAPTER 4Probability: The Study of Randomness 288

4.1 Randomness 290

The language of probability 290

Thinking about randomness 291

The uses of probability 292

Section 4.1 Exercises 293

4.2 Probability Models 295

Sample spaces 295

Intuitive probability 297

Probability rules 298

Assigning probabilities:nite number of outcomes 299

Assigning probabilities: equally likely outcomes 300

Independence and the multiplication rule 301

Applying the probability rules 304

Section 4.2 Exercises 306

4.3 Random Variables 312

Discrete random variables 313

Continuous random variables 317

Normal distributions as probability distributions 320

Section 4.3 Exercises 322

4.4 Means and Variances of Random Variables 326

The mean of a random variable 326

Statistical estimation and the law of large numbers 328

Thinking about the law of large numbers 331

Beyond the basics: more laws of large numbers 333

Rules for means 333

The variance of a random variable 335

Rules for variances 337

Section 4.4 Exercises 340

4.5 General Probability Rules 346

General addition rules 347

Conditional probability 350

General multiplication rules 353

Tree diagrams 354

Bayes's rule 355

Independence again 356

Decision analysis 357

Section 4.5 Exercises 359

Chapter 4 Exercises 365

CHAPTER 5From Probability to Inference 373

Introduction 374

5.1 Sampling Distributions for Counts and Proportions 375

The binomial distributions for sample counts 375

Binomial distributions in statistical sampling 377

Finding binomial probabilities: tables 378

Binomial mean and standard deviation 380

Sample proportions 381

Normal approximation for counts and proportions 382

The continuity correction 386

Binomial formulas 387

Section 5.1 Exercises 390

5.2 The Sampling Distribution of a Sample Mean 397

The mean and standard deviation of x 398

The sampling distribution of x 400

The central limit theorem 401

Beyond the basics: Weibull distributions 406

Section 5.2 Exercises 408

5.3 Control Charts 415

x control charts 415

Statistical process control 418

Using control charts 419

Section 5.3 Exercises 422

Chapter 5 Exercises 428

CHAPTER 6Introduction to Inference 432

Introduction 434

6.1 Estimating with Confidence 435

Statistical confidence 435

Confidence intervals 437

Confidence interval for a population mean 439

How confidence intervals behave 441

Choosing the sample size 443

Some cautions 444

Beyond the basics: the bootstrap 445

Section 6.1 Exercises 447

6.2 Tests of Significance 453

The reasoning of signicance tests 453

Stating hypotheses 454

Test statistics 456

P -values 457

Statistical significance 458

Tests for a population mean 460

Two-sided significance tests and confidence intervals 463

P -values versus fixed a 466

Section 6.2 Exercises 468

6.3 Use and Abuse of Tests 475

Choosing a level of significance 476

What statistical significance doesn't mean 477

Don't ignore lack of significance 478

Statistical inference is not valid for all sets of data 479

Beware of searching for significance 479

Section 6.3 Exercises 481

6.4 Power and Inference as a Decision 483

Power 483

Increasing the power 486

Inference as decision 488

Two types of error 488

Error probabilities 490

The common practice of testing hypotheses 492

Section 6.4 Exercises 493

Chapter 6 Exercises 496

CHAPTER 7Inference for Distributions 502

7.1 Inference for the Mean of a Population 504

The t distributions 504

The one-sample t condence interval 505

The one-sample t test 507

Matched pairs t procedures 513

Robustness of the t procedures 515

The power of the t test 517

Inference for nonnormal populations 518

The sign test 519

Section 7.1 Exercises 523

7.2 Comparing Two Means 537

The two-sample z statistic 538

The two-sample t procedures 540

The two-sample t signicance test 541

The two-sample t condence interval 544

Robustness of the two-sample procedures 545

Inference for small samples 546

Software approximation for the degrees of freedom 549

The pooled two-sample t procedures 550

Section 7.2 Exercises 556

7.3 Optional Topics in Comparing Distributions 566

Inference for population spread 566

The F test for equality of spread 567

Robustness of normal inference procedures 570

The power of the two-sample t -test 570

Section 7.3 Exercises 573

Chapter 7 Exercises 575

CHAPTER 8Inference for Proportions 584

8.1 Inference for a Single Proportion 586

Condence interval for a single proportion 586

Significance test for a single proportion 588

Confidence intervals provide additional information 591

Choosing a sample size 592

Section 8.1 Exercises 596

8.2 Comparing Two Proportions 601

Confidence intervals 602

Significance tests 604

Beyond the basics: relative risk 607

Section 8.2 Exercises 609

Chapter 8 Exercises 615

PART Ⅲ: Topics in Inference 621

CHAPTER 9Inference for Two-Way Tables 622

9.1 Inference for Two-Way Tables 624

The two-way table 624

Describing relations in two-way tables 626

The hypothesis: no association 628

Expected cell counts 629

The chi-square test 629

The chi-square test and the z test 632

Beyond the basics: meta-analysis 632

9.2 Formulas and Models for Two-Way Tables 634

Computations 634

Computing conditional distributions 635

Computing expected cell counts 637

Computing the chi-square statistic 638

Models for two-way tables 639

Comparing several populations: the first model 640

Testing independence: the second model 641

Concluding remarks 642

chapter9 Exercses 643

CHAPTER 10Inference for Regression 660

10.1 Simple Linear Regression 662

Statistical model for linear regression 662

Data for simple linear regression 663

Estimating the regression parameters 666

Confidence intervals and signicance tests 671

Confidence intervals for mean response 673

Prediction intervals 676

Beyond the basics: nonlinear regression 678

10.2 More Detail about Simple Linear Regression 681

Analysis of variance for regression 681

The ANOVA F 684

Calculations for regression inference 686

Preliminary calculations 687

Inference for slope and intercept 688

Confidence intervals for the mean response and predictionintervals for a future observation 690

Inference for correlation 691

chapter10Exercises 695

CHAPTER 11Multiple Regression 710

Population multiple regression equation 712

Data for multiple regression 713

Multiple linear regression model 713

Estimation of the multiple regression parameters 714

Confidence intervals and significance tests for regressioncoeficients 715

ANOVA table for multiple regression 717

Squared multiple correlation R 2 718

A case study: preliminary analysis 719

Relationships between pairs of variables 721

Regression on high school grades 722

Interpretation of results 723

Residuals 724

Refining the model 724

Regression on SAT scores 726

Regression using all variables 726

Test for a collection of regression coefficients 728

Beyond the basics: multiple logistic regression 730

Chapter 11 Exercises 732

CHAPTER 12One-Way Analysis of Variance 742

Data for a one-way ANOVA 744

Comparing means 745

The two-sample t statistic 747

ANOVA hypotheses 747

The ANOVA model 750

Estimates of population parameters 751

Testing hypotheses in one-way ANOVA 753

The ANOVA table 757

The F test 759

Contrasts 762

Multiple comparisons 769

Software 773

Power 775

Chapter 12 Exercises 779

CHAPTER 13Two-Way Analysis of Variance 798

Advantages of two-way ANOVA 800

The two-way ANOVA model 803

Main effects and interactions 804

The ANOVA table for two-way ANOVA 809

Chapter 13 Exercises 815