# Basic Statistics for Business & Economics, 10th Edition PDF by Douglas A Lind, William G Marchal and Samuel A Wathen

## Basic Statistics for Business & Economics, Tenth 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

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

Software Solution 63

EXERCISES 63

The Relative Positions of the Mean, Median, and Mode 65

EXERCISES 67

The Weighted Mean 67

EXERCISES 69

Why Study Dispersion? 69

Range 70

Variance 70

EXERCISES 72

Population Variance 73

Population Standard Deviation 75

EXERCISES 76

Sample Variance and Standard Deviation 76

Software Solution 78

EXERCISES 78

Interpretation and Uses of the Standard Deviation 79

Chebyshev’s Theorem 79

The Empirical Rule 79

EXERCISES 81

Ethics and Reporting Results 81

Chapter Summary 82

Chapter Exercises 83

Data Analytics 87

Practice Test 87

4. Describing Data DISPLAYING AND EXPLORING DATA 89

Introduction 90

Dot Plots 90

EXERCISES 92

Measures of Position 93

Quartiles, Deciles, and Percentiles 93

EXERCISES 97

Box Plots 97

EXERCISES 100

Skewness 101

EXERCISES 104

Describing the Relationship between Two Variables 105

Correlation Coefficient 106

Contingency Tables 108

EXERCISES 110

Chapter Summary 111

Chapter Exercises 112

Data Analytics 117

Practice Test 118

5. A Survey of Probability Concepts 119

Introduction 120

What Is a Probability? 121

Approaches to Assigning Probabilities 123

Classical Probability 123

Empirical Probability 124

Subjective Probability 126

EXERCISES 127

Rules of Addition for Computing Probabilities 128

Complement Rule 130

The General Rule of Addition 131

EXERCISES 133

Rules of Multiplication to Calculate Probability 134

Special Rule of Multiplication 134

General Rule of Multiplication 136

Contingency Tables 137

Tree Diagrams 141

EXERCISES 143

Principles of Counting 144

The Multiplication Formula 144

The Permutation Formula 146

The Combination Formula 148

EXERCISES 149

Chapter Summary 150

Chapter Exercises 151

Data Analytics 156

Practice Test 157

6. Discrete Probability Distributions 158

Introduction 159

What Is a Probability Distribution? 159

Random Variables 161

Discrete Random Variable 162

Continuous Random Variable 163

The Mean, Variance, and Standard Deviation of a Discrete

Probability Distribution 163

Mean 163

Variance and Standard Deviation 164

EXERCISES 166

Binomial Probability Distribution 167

How Is a Binomial Probability Computed? 169

Binomial Probability Tables 171

EXERCISES 174

Cumulative Binomial Probability Distributions 175

EXERCISES 177

Poisson Probability Distribution 177

EXERCISES 182

Chapter Summary 182

Chapter Exercises 183

Data Analytics 187

Practice Test 187

7. Continuous Probability Distributions 189

Introduction 190

The Family of Uniform Probability Distributions 190

EXERCISES 193

The Family of Normal Probability Distributions 194

The Standard Normal Probability Distribution 197

Applications of the Standard Normal Distribution 198

The Empirical Rule 198

EXERCISES 200

Finding Areas under the Normal Curve 201

EXERCISES 204

EXERCISES 206

EXERCISES 209

Chapter Summary 209

Chapter Exercises 210

Data Analytics 213

Practice Test 214

8. Sampling, Sampling Methods, and the Central Limit Theorem 215

Introduction 216

Research and Sampling 216

Sampling Methods 217

Simple Random Sampling 217

Systematic Random Sampling 220

Stratified Random Sampling 221

Cluster Sampling 222

EXERCISES 223

Sample Mean as a Random Variable 225

Sampling Distribution of the Sample Mean 226

EXERCISES 230

The Central Limit Theorem 231

Standard Error of the Mean 237

EXERCISES 237

Using the Sampling Distribution of the Sample Mean 239

EXERCISES 241

Chapter Summary 241

Chapter Exercises 242

Data Analytics 247

Practice Test 248

9. Estimation and Confidence Intervals 249

Introduction 250

Point Estimate for a Population Mean 250

Confidence Intervals for a Population Mean 251

Population Standard Deviation, Known σ 251

A Computer Simulation 256

EXERCISES 258

Population Standard Deviation, σ Unknown 259

EXERCISES 266

A Confidence Interval for a Population Proportion 267

EXERCISES 270

Choosing an Appropriate Sample Size 270

Sample Size to Estimate a Population Mean 271

Sample Size to Estimate a Population Proportion 272

EXERCISES 274

Chapter Summary 274

Chapter Exercises 275

Data Analytics 279

Practice Test 280

10. One-Sample Tests of Hypothesis 281

Introduction 282

What Is Hypothesis Testing? 282

Six-Step Procedure for Testing a Hypothesis 283

Step 1: State the Null Hypothesis (H0) and the Alternate Hypothesis (H1) 283

Step 2: Select a Level of Significance 284

Step 3: Select the Test Statistic 286

Step 4: Formulate the Decision Rule 286

Step 5: Make a Decision 287

Step 6: Interpret the Result 287

One-Tailed and Two-Tailed Hypothesis Tests 288

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

A Two-Tailed Test 290

A One-Tailed Test 293

p-Value in Hypothesis Testing 294

EXERCISES 296

Hypothesis Testing for a Population Mean: Population Standard Deviation Unknown 297

EXERCISES 300

A Statistical Software Solution 301

EXERCISES 303

Chapter Summary 304

Chapter Exercises 305

Data Analytics 308

Practice Test 309

11. Two-Sample Tests of Hypothesis 310

Introduction 311

Two-Sample Tests of Hypothesis: Independent Samples 311

EXERCISES 316

Comparing Population Means with Unknown Population Standard Deviations 317

Two-Sample Pooled Test 317

EXERCISES 321

Unequal Population Standard Deviations 323

EXERCISES 326

Two-Sample Tests of Hypothesis: Dependent Samples 327

Comparing Dependent and Independent Samples 330

EXERCISES 333

Chapter Summary 334

Chapter Exercises 336

Data Analytics 344

Practice Test 345

12. Analysis of Variance 346

Introduction 347

Comparing Two Population Variances 347

The F-Distribution 347

Testing a Hypothesis of Equal Population Variances 348

EXERCISES 352

ANOVA: Analysis of Variance 352

ANOVA Assumptions 353

The ANOVA Test 354

EXERCISES 361

Inferences about Pairs of Treatment Means 362

EXERCISES 365

Chapter Summary 367

Chapter Exercises 368

Data Analytics 375

Practice Test 376

13. Correlation and Linear Regression 13

Introduction 379

What Is Correlation Analysis? 379

The Correlation Coefficient 382

EXERCISES 387

Testing the Significance of the Correlation Coefficient 389

EXERCISES 392

Regression Analysis 393

Least Squares Principle 393

Drawing the Regression Line 396

EXERCISES 399

Testing the Significance of the Slope 401

EXERCISES 403

Evaluating a Regression Equation’s Ability to Predict 404

The Standard Error of Estimate 404

The Coefficient of Determination 405

EXERCISES 406

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

Estimate 406

EXERCISES 408

Interval Estimates of Prediction 409

Assumptions Underlying Linear Regression 409

Constructing Confidence and Prediction Intervals 410

EXERCISES 413

Transforming Data 413

EXERCISES 416

Chapter Summary 418

Chapter Exercises 420

Data Analytics 429

Practice Test 430

14. Multiple Regression Analysis 431

Introduction 432

Multiple Regression Analysis 432

EXERCISES 436

Evaluating a Multiple Regression Equation 438

The ANOVA Table 438

Multiple Standard Error of Estimate 439

Coefficient of Multiple Determination 440

EXERCISES 442

Inferences in Multiple Linear Regression 442

Global Test: Testing the Multiple Regression Model 442

Evaluating Individual Regression Coefficients 445

EXERCISES 448

Evaluating the Assumptions of Multiple Regression 449

Linear Relationship 450

Variation in Residuals Same for Large and Small ŷ Values 451

Distribution of Residuals 452

Multicollinearity 452

Independent Observations 454

Qualitative Independent Variables 455

Stepwise Regression 458

EXERCISES 460

Review of Multiple Regression 461

Chapter Summary 467

Chapter Exercises 469

Data Analytics 479

Practice Test 480

Appendix A:

Appendix B:

Appendix C:

15. Nonparametric Methods: NOMINAL LEVEL HYPOTHESIS TESTS 482

Introduction 483

Test a Hypothesis of a Population Proportion 483

EXERCISES 486

EXERCISES 491

Goodness-of-Fit Tests: Comparing Observed and

Expected Frequency Distributions 492

Hypothesis Test of Equal Expected Frequencies 492

EXERCISES 497

Hypothesis Test of Unequal Expected Frequencies 499

Limitations of Chi-Square 500

EXERCISES 502

Contingency Table Analysis 503

EXERCISES 506

Chapter Summary 507

Chapter Exercises 508

Data Analytics 513

Practice Test 514

APPENDIXES 515

Data Sets 516

Tables 524

Answers to Odd-Numbered Chapter Exercises & Solutions to Practice Test 537

Glossary 589

Index 593

Key Formulas 605

Areas under the Normal Curve 609

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