# Basic Statistics For Business & Economics, 9th Edition PDF by Douglas A Lind, William G Marchal and Samuel A Wathen

## Basic Statistics For Business & Economics, Ninth Edition

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

A note from the Authors vi

Preface vii

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 13

Chapter Exercises 14

Data Analytics 17

Practice Test 17

2 Describing Data:

FREQUENCY TABLES, FREQUENCY

DISTRIBUTIONS, AND GRAPHIC

PRESENTATION 19

Introduction 20

Constructing Frequency Tables 20

Relative Class Frequencies 21

Graphic Presentation of Qualitative Data 22

EXERCISES 26

Constructing Frequency Distributions 27

Relative Frequency Distribution 31

EXERCISES 32

Graphic Presentation of a Distribution 33

Histogram 33

Frequency Polygon 36

EXERCISES 38

Cumulative Distributions 39

EXERCISES 42

Chapter Summary 43

Chapter Exercises 44

Data Analytics 51

Practice Test 51

3 Describing Data:

NUMERICAL MEASURES 53

Introduction 54

Measures of Location 54

The Population Mean 55

The Sample Mean 56

Properties of the Arithmetic Mean 57

EXERCISES 58

The Median 59

The Mode 61

EXERCISES 63

The Relative Positions of the Mean, Median, and Mode 64

EXERCISES 65

Software Solution 66

The Weighted Mean 67

EXERCISES 68

Why Study Dispersion? 68

Range 69

Variance 70

EXERCISES 72

Population Variance 73

Population Standard Deviation 75

EXERCISES 75

Sample Variance and Standard

Deviation 76

Software Solution 77

EXERCISES 78

Interpretation and Uses of the Standard Deviation 78

Chebyshev’s Theorem 78

The Empirical Rule 79

EXERCISES 80

Ethics and Reporting Results 81

Chapter Summary 81

Chapter Exercises 83

Data Analytics 86

Practice Test 86

4 Describing Data:

DISPLAYING AND EXPLORING DATA 88

Introduction 89

Dot Plots 89

EXERCISES 91

Measures of Position 92

Quartiles, Deciles, and Percentiles 92

EXERCISES 96

Box Plots 96

EXERCISES 99

Skewness 100

EXERCISES 103

Describing the Relationship between Two Variables 104

Contingency Tables 106

EXERCISES 108

Chapter Summary 109

Pronunciation Key 110

Chapter Exercises 110

Data Analytics 115

Practice Test 115

5 A Survey of Probability Concepts 117

Introduction 118

What is a Probability? 119

Approaches to Assigning Probabilities 121

Classical Probability 121

Empirical Probability 122

Subjective Probability 124

EXERCISES 125

Rules of Addition for Computing Probabilities 126

Complement Rule 128

The General Rule of Addition 129

EXERCISES 131

Rules of Multiplication to Calculate Probability 132

Special Rule of Multiplication 132

General Rule of Multiplication 133

Contingency Tables 135

Tree Diagrams 138

EXERCISES 140

Principles of Counting 142

The Multiplication Formula 142

The Permutation Formula 143

The Combination Formula 145

EXERCISES 147

Chapter Summary 147

Pronunciation Key 148

Chapter Exercises 148

Data Analytics 153

Practice Test 154

6 Discrete Probability Distributions 155

Introduction 156

What is a Probability Distribution? 156

Random Variables 158

Discrete Random Variable 159

Continuous Random Variable 160

The Mean, Variance, and Standard Deviation of a

Discrete Probability Distribution 160

Mean 160

Variance and Standard Deviation 160

EXERCISES 162

Binomial Probability Distribution 164

How is a Binomial Probability Computed? 165

Binomial Probability Tables 167

EXERCISES 170

Cumulative Binomial Probability Distributions 171

EXERCISES 172

Poisson Probability Distribution 173

EXERCISES 178

Chapter Summary 178

Chapter Exercises 179

Data Analytics 183

Practice Test 183

7 Continuous Probability

Distributions 184

Introduction 185

The Family of Uniform Probability Distributions 185

EXERCISES 188

The Family of Normal Probability Distributions 189

The Standard Normal Probability Distribution 192

Applications of the Standard Normal Distribution 193

The Empirical Rule 193

EXERCISES 195

Finding Areas under the Normal Curve 196

EXERCISES 199

EXERCISES 201

EXERCISES 204

Chapter Summary 204

Chapter Exercises 205

Data Analytics 208

Practice Test 209

8 Sampling Methods and the Central Limit Theorem 210

Introduction 211

Sampling Methods 211

Reasons to Sample 211

Simple Random Sampling 212

Systematic Random Sampling 215

Stratified Random Sampling 215

Cluster Sampling 216

EXERCISES 217

Sampling “Error” 219

Sampling Distribution of the Sample Mean 221

EXERCISES 224

The Central Limit Theorem 225

EXERCISES 231

Using the Sampling Distribution of the Sample Mean 232

EXERCISES 234

Chapter Summary 235

Pronunciation Key 236

Chapter Exercises 236

Data Analytics 241

Practice Test 241

9 Estimation and Confidence Intervals 242

Introduction 243

Point Estimate for a Population Mean 243

Confidence Intervals for a Population Mean 244

Population Standard Deviation, Known σ 244

A Computer Simulation 249

EXERCISES 251

Population Standard Deviation, σ Unknown 252

EXERCISES 259

A Confidence Interval for a Population Proportion 260

EXERCISES 263

Choosing an Appropriate Sample Size 263

Sample Size to Estimate a Population Mean 264

Sample Size to Estimate a Population Proportion 265

EXERCISES 267

Chapter Summary 267

Chapter Exercises 268

Data Analytics 272

Practice Test 273

10 One-Sample Tests of Hypothesis 274

Introduction 275

What is Hypothesis Testing? 275

Six-Step Procedure for Testing a Hypothesis 276

Step 1: State the Null Hypothesis (H0) and the Alternate Hypothesis (H1) 276

Step 2: Select a Level of Significance 277

Step 3: Select the Test Statistic 279

Step 4: Formulate the Decision Rule 279

Step 5: Make a Decision 280

Step 6: Interpret the Result 280

One-Tailed and Two-Tailed Hypothesis Tests 281

Hypothesis Testing for a Population Mean: Known

Population Standard Deviation 283

A Two-Tailed Test 283

A One-Tailed Test 286

p-Value in Hypothesis Testing 287

EXERCISES 289

Hypothesis Testing for a Population Mean: Population Standard Deviation Unknown 290

EXERCISES 295

A Statistical Software Solution 296

EXERCISES 297

Chapter Summary 299

Pronunciation Key 299

Chapter Exercises 300

Data Analytics 303

Practice Test 303

11 Two-Sample Tests of Hypothesis 305

Introduction 306

Two-Sample Tests of Hypothesis: Independent Samples 306

EXERCISES 311

Comparing Population Means with Unknown Population Standard Deviations 312

Two-Sample Pooled Test 312

EXERCISES 316

Two-Sample Tests of Hypothesis:

Dependent Samples 318

Comparing Dependent

and Independent Samples 321

EXERCISES 324

Chapter Summary 325

Pronunciation Key 326

Chapter Exercises 326

Data Analytics 332

Practice Test 332

12 Analysis of Variance 334

Introduction 335

Comparing Two Population Variances 335

The F Distribution 335

Testing a Hypothesis of Equal Population Variances 336

EXERCISES 339

ANOVA: Analysis of Variance 340

ANOVA Assumptions 340

The ANOVA Test 342

EXERCISES 349

Inferences about Pairs of Treatment Means 350

EXERCISES 352

Chapter Summary 354

Pronunciation Key 355

Chapter Exercises 355

Data Analytics 362

Practice Test 363

13 Correlation and

Linear Regression 365

Introduction 366

What is Correlation Analysis? 366

The Correlation Coefficient 369

EXERCISES 374

Testing the Significance of the Correlation Coefficient 376

EXERCISES 379

Regression Analysis 380

Least Squares Principle 380

Drawing the Regression Line 383

EXERCISES 386

Testing the Significance of the Slope 388

EXERCISES 390

Evaluating a Regression Equation’s Ability to Predict 391

The Standard Error of Estimate 391

The Coefficient of Determination 392

EXERCISES 393

Relationships among the Correlation Coefficient, the Coefficient of Determination, and the Standard

Error of Estimate 393

EXERCISES 395

Interval Estimates of Prediction 396

Assumptions Underlying Linear Regression 396

Constructing Confidence and Prediction Intervals 397

EXERCISES 400

Transforming Data 400

EXERCISES 403

Chapter Summary 404

Pronunciation Key 406

Chapter Exercises 406

Data Analytics 415

Practice Test 416

14 Multiple Regression

Analysis 418

Introduction 419

Multiple Regression Analysis 419

EXERCISES 423

Evaluating a Multiple Regression Equation 425

The ANOVA Table 425

Multiple Standard Error of Estimate 426

Coefficient of Multiple Determination 427

EXERCISES 429

Inferences in Multiple Linear Regression 429

Global Test: Testing the Multiple Regression Model 429

Evaluating Individual Regression Coefficients 432

EXERCISES 435

Evaluating the Assumptions of Multiple Regression 436

Linear Relationship 437

Variation in Residuals Same for Large and Small ŷ Values 438

Distribution of Residuals 439

Multicollinearity 439

Independent Observations 441

Qualitative Independent Variables 442

Stepwise Regression 445

EXERCISES 447

Review of Multiple Regression 448

Chapter Summary 454

Pronunciation Key 455

Chapter Exercises 456

Data Analytics 466

Practice Test 467

15 Nonparametric Methods:

NOMINAL-LEVEL HYPOTHESIS TESTS 469

Introduction 470

Test a Hypothesis of a Population Proportion 470

EXERCISES 473

EXERCISES 478

Goodness-of-Fit Tests: Comparing Observed and

Expected Frequency Distributions 479

Hypothesis Test of Equal Expected Frequencies 479

EXERCISES 484

Hypothesis Test of Unequal Expected

Frequencies 486

Limitations of Chi-Square 487

EXERCISES 489

Contingency Table Analysis 490

EXERCISES 493

Chapter Summary 494

Pronunciation Key 495

Chapter Exercises 495

Data Analytics 500

Practice Test 501

APPENDIXES 503

Appendix A: Data Sets 504

Appendix B: Tables 513

Appendix C: Software Commands 526

Chapter Exercises 534

Solutions to Practice Tests 566

Appendix E: Answers to Self-Review 570

Glossary 578

Index 581

Key Formulas

Student’s t Distribution

Areas under the Normal Curve

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