# Statistical Techniques in Business & Economics, 18th Edition PDF by Douglas A Lind, William G Marchal and Samuel A Wathen

## Statistical Techniques in Business & Economics, Eighteenth Edition

By Douglas A. Lind, William G. Marchal and Samuel A. Wathen Contents:

A Note from the Authors  vi

1 What Is Statistics? 1

Introduction 2

Why Study Statistics? 2

What Is Meant by Statistics? 3

Types of Statistics 4

Descriptive Statistics 4

Inferential Statistics 5

Types of Variables 6

Levels of Measurement 7

Nominal-Level Data 7

Ordinal-Level Data 8

Interval-Level Data 9

Ratio-Level Data 10

EXERCISES 11

Ethics and Statistics 12

Chapter Summary 14

Chapter Exercises 14

Data Analytics 17

2 Describing Data:

Frequency Tables, Frequency Distributions, and Graphic Presentation 18

Introduction 19

Constructing Frequency Tables 19

Relative Class Frequencies 20

Graphic Presentation of Qualitative Data 21

EXERCISES 25

Constructing Frequency Distributions 26

Relative Frequency Distribution 30

EXERCISES 31

Graphic Presentation of a Distribution 32

Histogram 32

Frequency Polygon 35

EXERCISES 37

Cumulative Distributions 38

EXERCISES 41

Chapter Summary 42

Chapter Exercises 43

Data Analytics 50

3 Describing Data:

Numerical Measures 51

Introduction 52

Measures of Location 52

The Population Mean 53

The Sample Mean 54

Properties of the Arithmetic Mean 55

EXERCISES 56

The Median 57

The Mode 59

Software Solution 61

EXERCISES 61

The Relative Positions of the Mean, Median, and Mode 63

EXERCISES 65

The Weighted Mean 65

EXERCISES 67

The Geometric Mean 67

EXERCISES 69

Why Study Dispersion? 69

Range 70

Variance 71

EXERCISES 73

Population Variance 74

Population Standard Deviation 76

EXERCISES 76

Sample Variance and Standard Deviation 77

Software Solution 78

EXERCISES 79

Interpretation and Uses of the

Standard Deviation 79

Chebyshev’s Theorem 79

The Empirical Rule 80

EXERCISES 81

The Mean and Standard Deviation of Grouped Data 82

Arithmetic Mean of Grouped Data 82

Standard Deviation of Grouped Data 83

EXERCISES 85

Ethics and Reporting Results 86

Chapter Summary 86

Chapter Exercises 88

Data Analytics 92

4 Describing Data:

Displaying and Exploring Data 94

Introduction 95

Dot Plots 95

EXERCISES 97

Measures of Position 98

Quartiles, Deciles, and Percentiles 98

EXERCISES 102

Box Plots 102

EXERCISES 105

Skewness 106

EXERCISES 109

Describing the Relationship between

Two Variables 110

Correlation Coefficient 111

Contingency Tables 113

EXERCISES 115

Chapter Summary 116

Pronunciation Key 117

Chapter Exercises 117

Data Analytics 123

A REVIEW OF CHAPTERS 1–4 123

PROBLEMS 124

CASES 126

PRACTICE TEST 127

5 A Survey of Probability Concepts 130

Introduction 131

What Is a Probability? 132

Approaches to Assigning Probabilities 134

Classical Probability 134

Empirical Probability 135

Subjective Probability 137

EXERCISES 138

Rules of Addition for Computing Probabilities 139

Complement Rule 141

The General Rule of Addition 142

EXERCISES 144

Rules of Multiplication to Calculate Probability 145

Special Rule of Multiplication 145

General Rule of Multiplication 146

Contingency Tables 148

Tree Diagrams 151

EXERCISES 153

Bayes’ Theorem 155

EXERCISES 159

Principles of Counting 159

The Multiplication Formula 159

The Permutation Formula 161

The Combination Formula 163

EXERCISES 165

Chapter Summary 165

Pronunciation Key 166

Chapter Exercises 166

Data Analytics 172

6 Discrete Probability

Distributions 173

Introduction 174

What Is a Probability Distribution? 174

Random Variables 176

Discrete Random Variable 177

Continuous Random Variable 178

The Mean, Variance, and Standard Deviation of a Discrete Probability Distribution 178

Mean 178

Variance and Standard Deviation 179

EXERCISES 181

Binomial Probability Distribution 182

How Is a Binomial Probability Computed? 184

Binomial Probability Tables 186

EXERCISES 189

Cumulative Binomial Probability Distributions 190

EXERCISES 192

Hypergeometric Probability Distribution 192

EXERCISES 196

Poisson Probability Distribution 196

EXERCISES 201

Chapter Summary 201

Chapter Exercises 202

Data Analytics 207

7 Continuous Probability

Distributions 208

Introduction 209

The Family of Uniform Probability

Distributions 209

EXERCISES 212

The Family of Normal Probability Distributions 213

The Standard Normal Probability Distribution 216

Applications of the Standard Normal Distribution 217

The Empirical Rule 217

EXERCISES 219

Finding Areas under the Normal Curve 220

EXERCISES 223

EXERCISES 225

EXERCISES 228

The Family of Exponential Distributions 228

EXERCISES 233

Chapter Summary 234

Chapter Exercises 235

Data Analytics 238

A REVIEW OF CHAPTERS 5–7 239

PROBLEMS 239

CASES 241

PRACTICE TEST 242

8 Sampling, Sampling

Methods, and the Central Limit Theorem 244

Introduction 245

Research and Sampling 245

Sampling Methods 246

Simple Random Sampling 246

Systematic Random Sampling 249

Stratified Random Sampling 250

Cluster Sampling 251

EXERCISES 252

Sample Mean as a Random Variable 254

Sampling Distribution of the Sample Mean 255

EXERCISES 259

The Central Limit Theorem 260

Standard Error of The Mean 266

EXERCISES 266

Using the Sampling Distribution of the Sample Mean 267

EXERCISES 270

Chapter Summary 270

Pronunciation Key 271

Chapter Exercises 271

Data Analytics 276

9 Estimation and Confidence

Intervals 277

Introduction 278

Point Estimate for a Population Mean 278

Confidence Intervals for a Population Mean 279

Population Standard Deviation, Known σ 279

A Computer Simulation 284

EXERCISES 286

Population Standard Deviation, σ Unknown 287

EXERCISES 294

A Confidence Interval for a Population Proportion 295

EXERCISES 298

Choosing an Appropriate Sample Size 298

Sample Size to Estimate a Population Mean 299

Sample Size to Estimate a Population

Proportion 300

EXERCISES 302

Finite-Population Correction Factor 302

EXERCISES 304

Chapter Summary 305

Chapter Exercises 306

Data Analytics 310

A REVIEW OF CHAPTERS 8–9 310

PROBLEMS 311

CASES 312

PRACTICE TEST 312

10 One-Sample Tests of Hypothesis 314

Introduction 315

What Is Hypothesis Testing? 315

Six-Step Procedure for Testing a Hypothesis 316

Step 1: State the Null Hypothesis (H0) and the Alternate Hypothesis (H1) 316

Step 2: Select a Level of Significance 317

Step 3: Select the Test Statistic 319

Step 4: Formulate the Decision Rule 319

Step 5: Make a Decision 320

Step 6: Interpret the Result 320

One-Tailed and Two-Tailed Hypothesis Tests 321

Hypothesis Testing for a Population Mean: Known Population Standard Deviation 323

A Two-Tailed Test 323

A One-Tailed Test 326

p-Value in Hypothesis Testing 327

EXERCISES 329

Hypothesis Testing for a Population Mean: Population Standard Deviation Unknown 330

EXERCISES 333

A Statistical Software Solution 334

EXERCISES 336

Type II Error 337

EXERCISES 340

Chapter Summary 341

Pronunciation Key 342

Chapter Exercises 342

Data Analytics 346

11 Two-Sample Tests of Hypothesis 347

Introduction 348

Two-Sample Tests of Hypothesis: Independent Samples 348

EXERCISES 353

Comparing Population Means with Unknown Population Standard Deviations 354

Two-Sample Pooled Test 354

EXERCISES 358

Unequal Population Standard Deviations 360

EXERCISES 363

Two-Sample Tests of Hypothesis: Dependent Samples 364

Comparing Dependent and Independent Samples 367

EXERCISES 370

Chapter Summary 371

Pronunciation Key 372

Chapter Exercises 373

Data Analytics 381

12 Analysis of Variance 382

Introduction 383

Comparing Two Population Variances 383

The F-Distribution 383

Testing a Hypothesis of Equal Population Variances 384

EXERCISES 387

ANOVA: Analysis of Variance 388

ANOVA Assumptions 388

The ANOVA Test 390

EXERCISES 397

Inferences about Pairs of Treatment Means 398

EXERCISES 401

Two-Way Analysis of Variance 403

EXERCISES 407

Two-Way ANOVA with Interaction 408

Interaction Plots 409

Testing for Interaction 410

Hypothesis Tests for Interaction 411

EXERCISES 414

Chapter Summary 415

Pronunciation Key 416

Chapter Exercises 417

Data Analytics 427

A REVIEW OF CHAPTERS 10–12 427

PROBLEMS 428

CASES 431

PRACTICE TEST 431

13 Correlation and Linear Regression 433

Introduction 434

What Is Correlation Analysis? 434

The Correlation Coefficient 437

EXERCISES 442

Testing the Significance of the Correlation Coefficient 444

EXERCISES 447

Regression Analysis 448

Least Squares Principle 448

Drawing the Regression Line 451

EXERCISES 454

Testing the Significance of the Slope 456

EXERCISES 458

Evaluating a Regression Equation’s

Ability to Predict 459

The Standard Error of Estimate 459

The Coefficient of Determination 460

EXERCISES 461

Relationships among the Correlation

Coefficient, the Coefficient of

Determination, and the Standard

Error of Estimate 461

EXERCISES 463

Interval Estimates of Prediction 464

Assumptions Underlying Linear

Regression 464

Constructing Confidence and Prediction

Intervals 465

EXERCISES 468

Transforming Data 468

EXERCISES 471

Chapter Summary 473

Pronunciation Key 474

Chapter Exercises 475

Data Analytics 484

14 Multiple Regression

Analysis 485

Introduction 486

Multiple Regression Analysis 486

EXERCISES 490

Evaluating a Multiple Regression

Equation 492

The ANOVA Table 492

Multiple Standard Error of Estimate 493

Coefficient of Multiple Determination 494

EXERCISES 496

Inferences in Multiple Linear Regression 496

Global Test: Testing the Multiple

Regression Model 496

Evaluating Individual Regression

Coefficients 499

EXERCISES 502

Evaluating the Assumptions

of Multiple Regression 503

Linear Relationship 504

Variation in Residuals Same for Large and Small ŷ

Values 505

Distribution of Residuals 506

Multicollinearity 506

Independent Observations 508

Qualitative Independent Variables 509

Regression Models with Interaction 512

Stepwise Regression 514

EXERCISES 516

Review of Multiple Regression 518

Chapter Summary 524

Pronunciation Key 525

Chapter Exercises 526

Data Analytics 536

A REVIEW OF CHAPTERS 13–14 537

PROBLEMS 538

CASES 539

PRACTICE TEST 540

15 Nonparametric Methods:

Nominal Level Hypothesis Tests 542

Introduction 543

Test a Hypothesis of a Population

Proportion 543

EXERCISES 546

EXERCISES 551

Goodness-of-Fit Tests: Comparing

Observed and Expected Frequency

Distributions 552

Hypothesis Test of Equal Expected

Frequencies 552

EXERCISES 557

Hypothesis Test of Unequal Expected

Frequencies 559

Limitations of Chi-Square 560

EXERCISES 562

Testing the Hypothesis That a

Distribution Is Normal 563

EXERCISES 566

Contingency Table Analysis 567

EXERCISES 570

Chapter Summary 571

Pronunciation Key 572

Chapter Exercises 573

Data Analytics 578

16 Nonparametric Methods:

Analysis of Ordinal Data 579

Introduction 580

The Sign Test 580

EXERCISES 584

Testing a Hypothesis About a Median 585

EXERCISES 587

Wilcoxon Signed-Rank Test for Dependent Populations 587

EXERCISES 591

Wilcoxon Rank-Sum Test for Independent Populations 592

EXERCISES 596

Kruskal-Wallis Test: Analysis of Variance by Ranks 596

EXERCISES 600

Rank-Order Correlation 602

Testing the Significance of rs 605

EXERCISES 605

Chapter Summary 607

Pronunciation Key 608

Chapter Exercises 608

Data Analytics 611

A REVIEW OF CHAPTERS 15–16 612

PROBLEMS 613

CASES 614

PRACTICE TEST 614

17 Index Numbers 616

Introduction 617

Simple Index Numbers 617

Why Convert Data to Indexes? 620

Construction of Index Numbers 620

EXERCISES 622

Unweighted Indexes 623

Simple Average of the Price Indexes 623

Simple Aggregate Index 624

Weighted Indexes 624

Laspeyres Price Index 624

Paasche Price Index 626

Fisher’s Ideal Index 627

EXERCISES 628

Value Index 629

EXERCISES 630

Special-Purpose Indexes 631

Consumer Price Index 632

Producer Price Index 633

Dow Jones Industrial Average (DJIA) 633

EXERCISES 635

Consumer Price Index 635

Special Uses of the Consumer Price Index 636

Shifting the Base 639

EXERCISES 641

Chapter Summary 642

Chapter Exercises 643

Data Analytics 647

18 Forecasting with Time Series Analysis 648

Introduction 649

Time Series Patterns 649

Trend 649

Seasonality 651

Cycles 652

Irregular Component 652

EXERCISES 653

Modeling Stationary Time Series: Forecasts Using

Simple Moving Averages 653

Forecasting Error 655

EXERCISES 658

Modeling Stationary Time Series:

Simple Exponential Smoothing 659

EXERCISES 663

Modeling Time Series with Trend:

Regression Analysis 665

Regression Analysis 666

EXERCISES 672

The Durbin-Watson Statistic 673

EXERCISES 678

Modeling Time Series with Seasonality:

Seasonal Indexing 679

EXERCISES 687

Chapter Summary 689

Chapter Exercises 689

Data Analytics 693

A REVIEW OF CHAPTERS 17–18 695

PROBLEMS 696

PRACTICE TEST 697

19 Statistical Process Control and Quality Management 698

Introduction 699

A Brief History of Quality Control 699

Six Sigma 701

Sources of Variation 702

Diagnostic Charts 703

Pareto Charts 703

Fishbone Diagrams 705

EXERCISES 706

Purpose and Types of Quality Control Charts 706

Control Charts for Variables 707

Range Charts 710

In-Control and Out-of-Control Situations 712

EXERCISES 713

Attribute Control Charts 714

p-Charts 714

c-Bar Charts 717

EXERCISES 719

Acceptance Sampling 720

EXERCISES 723

Chapter Summary 723

Pronunciation Key 724

Chapter Exercises 725

20 An Introduction to

Decision Theory

Online Only www.mhhe.com/Lind18e

Introduction

Elements of a Decision

Decision Making Under Conditions of Uncertainty

Payoff Table

Expected Payoff

EXERCISES

Opportunity Loss

EXERCISES

Expected Opportunity Loss

EXERCISES

Maximin, Maximax, and Minimax Regret Strategies

Value of Perfect Information

Sensitivity Analysis

EXERCISES

Decision Trees

Chapter Summary

Chapter Exercises

APPENDIXES 729

Appendix A: Data Sets 730

Appendix B: Tables 740

Chapter Exercises 758

Review Exercises 813

Solutions to Practice Tests 815

Appendix D: Answers to Self-Review 818

Glossary 832

Index 836

This book is US\$10
To get free sample pages OR Buy this book
Send email: [email protected]