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# Principles of Managerial Statistics and Data Science. Edition No. 1

• ID: 5226457
• Book
• March 2020
• 688 Pages
• John Wiley and Sons Ltd
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Introduces readers to the principles of managerial statistics and data science, with an emphasis on statistical literacy of business students

Through a statistical perspective, this book introduces readers to the topic of data science, including Big Data, data analytics, and data wrangling. Chapters include multiple examples showing the application of the theoretical aspects presented. It features practice problems designed to ensure that readers understand the concepts and can apply them using real data. Over 100 open data sets used for examples and problems come from regions throughout the world, allowing the instructor to adapt the application to local data with which students can identify. Applications with these data sets include:

• Assessing if searches during a police stop in San Diego are dependent on driver’s race
• Visualizing the association between fat percentage and moisture percentage in Canadian cheese
• Modeling taxi fares in Chicago using data from millions of rides
• Analyzing mean sales per unit of legal marijuana products in Washington state

Topics covered in Principles of Managerial Statistics and Data Science include:data visualization; descriptive measures; probability; probability distributions; mathematical expectation; confidence intervals; and hypothesis testing. Analysis of variance; simple linear regression; and multiple linear regression are also included. In addition, the book offers contingency tables, Chi-square tests, non-parametric methods, and time series methods. The textbook:

• Includes academic material usually covered in introductory Statistics courses, but with a data science twist, and less emphasis in the theory
• Relies on Minitab to present how to perform tasks with a computer
• Presents and motivates use of data that comes from open portals
• Focuses on developing an intuition on how the procedures work
• Exposes readers to the potential in Big Data and current failures of its use
• Supplementary material includes: a companion website that houses PowerPoint slides; an Instructor's Manual with tips, a syllabus model, and project ideas; R code to reproduce examples and case studies; and information about the open portal data
• Features an appendix with solutions to some practice problems

Principles of Managerial Statistics and Data Science is a textbook for undergraduate and graduate students taking managerial Statistics courses, and a reference book for working business professionals.

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Preface xv

Acknowledgments xvii

Acronyms xix

Principles of Managerial Statistics and Data Science xxiii

1 Statistics Suck; So Why Do I Need to Learn About It? 1

1.1 Introduction 1

Practice Problems 4

1.2 Data-Based Decision Making: Some Applications 5

1.3 Statistics Defined 9

1.4 Use of Technology and the New Buzzwords: Data Science, Data Analytics, and Big Data 11

1.4.1 A Quick Look at Data Science: Some Definitions 11

Chapter Problems 14

2 Concepts in Statistics 15

2.1 Introduction 15

Practice Problems 17

2.2 Type of Data 19

Practice Problems 20

2.3 Four Important Notions in Statistics 22

Practice Problems 24

2.4 Sampling Methods 25

2.4.1 Probability Sampling 25

2.4.2 Nonprobability Sampling 27

Practice Problems 30

2.5 Data Management 31

2.5.1 A Quick Look at Data Science: Data Wrangling Baltimore Housing Variables 34

2.6 Proposing a Statistical Study 36

Chapter Problems 37

3 Data Visualization 41

3.1 Introduction 41

3.2 Visualization Methods for Categorical Variables 41

Practice Problems 46

3.3 Visualization Methods for Numerical Variables 50

Practice Problems 56

3.4 Visualizing Summaries of More than Two Variables Simultaneously 59

3.4.1 A Quick Look at Data Science: Does Race Affect the Chances of a Driver Being Searched During a Vehicle Stop in San Diego? 66

Practice Problems 69

3.5 Novel Data Visualization 75

3.5.1 A Quick Look at Data Science: Visualizing Association Between Baltimore Housing Variables Over 14 Years 78

Chapter Problems 81

4 Descriptive Statistics 97

4.1 Introduction 97

4.2 Measures of Centrality 99

Practice Problems 108

4.3 Measures of Dispersion 111

Practice Problems 115

4.4 Percentiles 116

4.4.1 Quartiles 117

Practice Problems 122

4.5 Measuring the Association Between Two Variables 124

Practice Problems 128

4.6 Sample Proportion and Other Numerical Statistics 130

4.6.1 A Quick Look at Data Science: Murder Rates in Los Angeles 131

4.7 How to Use Descriptive Statistics 132

Chapter Problems 133

5 Introduction to Probability 141

5.1 Introduction 141

5.2 Preliminaries 142

Practice Problems 144

5.3 The Probability of an Event 145

Practice Problems 148

5.4 Rules and Properties of Probabilities 149

Practice Problems 152

5.5 Conditional Probability and Independent Events 154

Practice Problems 159

5.6 Empirical Probabilities 161

5.6.1 A Quick Look at Data Science: Missing People Reports in Boston by Day of Week 164

Practice Problems 165

5.7 Counting Outcomes 168

Practice Problems 171

Chapter Problems 171

6 Discrete Random Variables 177

6.1 Introduction 177

6.2 General Properties 178

6.2.1 A Quick Look at Data Science: Number of Stroke Emergency Calls in Manhattan 183

Practice Problems 184

6.3 Properties of Expected Value and Variance 186

Practice Problems 189

6.4 Bernoulli and Binomial Random Variables 190

Practice Problems 197

6.5 Poisson Distribution 198

Practice Problems 201

6.6 Optional: Other Useful Probability Distributions 203

Chapter Problems 205

7 Continuous Random Variables 209

7.1 Introduction 209

Practice Problems 211

7.2 The Uniform Probability Distribution 211

Practice Problems 215

7.3 The Normal Distribution 216

Practice Problems 225

7.4 Probabilities for Any Normally Distributed Random Variable 227

7.4.1 A Quick Look at Data Science: Normal Distribution, A Good Match for University of Puerto Rico SATs? 229

Practice Problems 231

7.5 Approximating the Binomial Distribution 234

Practice Problems 236

7.6 Exponential Distribution 236

Practice Problems 238

Chapter Problems 239

8 Properties of Sample Statistics 243

8.1 Introduction 243

8.2 Expected Value and Standard Deviation of 244

Practice Problems 246

8.3 Sampling Distribution of When Sample Comes From a Normal Distribution 247

Practice Problems 251

8.4 Central Limit Theorem 252

8.4.1 A Quick Look at Data Science: Bacteria at New York City Beaches 257

Practice Problems 259

8.5 Other Properties of Estimators 261

Chapter Problems 264

9 Interval Estimation for One Population Parameter 269

9.1 Introduction 269

9.2 Intuition of a Two-Sided Confidence Interval 270

9.3 Confidence Interval for the Population Mean: 𝜎 Known 271

Practice Problems 276

9.4 Determining Sample Size for a Confidence Interval for 𝜇 278

Practice Problems 279

9.5 Confidence Interval for the Population Mean: 𝜎 Unknown 279

Practice Problems 284

9.6 Confidence Interval for 𝜋 286

Practice Problems 287

9.7 Determining Sample Size for 𝜋 Confidence Interval 288

Practice Problems 290

9.8 Optional: Confidence Interval for 𝜎 290

9.8.1 A Quick Look at Data Science: A Confidence Interval for the Standard Deviation of Walking Scores in Baltimore 292

Chapter Problems 293

10 Hypothesis Testing for One Population 297

10.1 Introduction 297

10.2 Basics of Hypothesis Testing 299

10.3 Steps to Perform a Hypothesis Test 304

Practice Problems 305

10.4 Inference on the Population Mean: Known Standard Deviation 306

Practice Problems 318

10.5 Hypothesis Testing for the Mean (𝜎 Unknown) 323

Practice Problems 327

10.6 Hypothesis Testing for the Population Proportion 329

10.6.1 A Quick Look at Data Science: Proportion of New York City High Schools with a Mean SAT Score of 1498 or More 333

Practice Problems 334

10.7 Hypothesis Testing for the Population Variance 337

10.8 More on the p-Value and Final Remarks 338

10.8.1 Misunderstanding the p-Value 339

Chapter Problems 343

11 Statistical Inference to Compare Parameters from Two Populations 349

11.1 Introduction 349

11.2 Inference on Two Population Means 350

11.3 Inference on Two Population Means – Independent Samples, Variances Known 351

Practice Problems 357

11.4 Inference on Two Population Means When Two Independent Samples are Used – Unknown Variances 360

11.4.1 A Quick Look at Data Science: Suicide Rates Among Asian Men and Women in New York City 364

Practice Problems 366

11.5 Inference on Two Means Using Two Dependent Samples 368

Practice Problems 370

11.6 Inference on Two Population Proportions 371

Practice Problems 374

Chapter Problems 375

References 378

12 Analysis of Variance (ANOVA) 379

12.1 Introduction 379

Practice Problems 382

12.2 ANOVA for One Factor 383

Practice Problems 390

12.3 Multiple Comparisons 391

Practice Problems 395

12.4 Diagnostics of ANOVA Assumptions 395

12.4.1 A Quick Look at Data Science: Emergency Response Time for Cardiac Arrest in New York City 399

Practice Problems 403

12.5 ANOVA with Two Factors 404

Practice Problems 409

12.6 Extensions to ANOVA 413

Chapter Problems 416

13 Simple Linear Regression 421

13.1 Introduction 421

13.2 Basics of Simple Linear Regression 423

Practice Problems 425

13.3 Fitting the Simple Linear Regression Parameters 426

Practice Problems 429

13.4 Inference for Simple Linear Regression 431

Practice Problems 440

13.5 Estimating and Predicting the Response Variable 443

Practice Problems 446

13.6 A Binary X 448

Practice Problems 449

13.7 Model Diagnostics (Residual Analysis) 450

Practice Problems 456

13.8 What Correlation Doesn’t Mean 458

13.8.1 A Quick Look at Data Science: Can Rate of College Educated People Help Predict the Rate of Narcotic Problems in Baltimore? 461

Chapter Problems 466

14 Multiple Linear Regression 473

14.1 Introduction 473

14.2 The Multiple Linear Regression Model 474

Practice Problems 477

14.3 Inference for Multiple Linear Regression 478

Practice Problems 483

14.4 Multicollinearity and Other Modeling Aspects 486

Practice Problems 490

14.5 Variability Around the Regression Line: Residuals and Intervals 492

Practice Problems 494

14.6 Modifying Predictors 494

Practice Problems 495

14.7 General Linear Model 496

Practice Problems 502

14.8 Steps to Fit a Multiple Linear Regression Model 505

14.9 Other Regression Topics 507

14.9.1 A Quick Look at Data Science: Modeling Taxi Fares in Chicago 510

Chapter Problems 513

15 Inference on Association of Categorical Variables 519

15.1 Introduction 519

15.2 Association Between Two Categorical Variables 520

15.2.1 A Quick Look at Data Science: Affordability and Business Environment in Chattanooga 525

Practice Problems 529

Chapter Problems 532

16 Nonparametric Testing 533

16.1 Introduction 533

16.2 Sign Tests and Wilcoxon Sign-Rank Tests: One Sample and Matched Pairs Scenarios 533

Practice Problems 537

16.3 Wilcoxon Rank-Sum Test: Two Independent Samples 539

16.3.1 A Quick Look at Data Science: Austin, Texas, as a Place to Live; Do Men Rate It Higher Than Women? 540

Practice Problems 543

16.4 Kruskal–Wallis Test: More Than Two Samples 544

Practice Problems 546

16.5 Nonparametric Tests Versus Their Parametric Counterparts 547

Chapter Problems 548

17 Forecasting 551

17.1 Introduction 551

17.2 Time Series Components 552

Practice Problems 557

17.3 Simple Forecasting Models 558

Practice Problems 562

17.4 Forecasting When Data Has Trend, Seasonality 563

Practice Problems 569

17.5 Assessing Forecasts 572

17.5.1 A Quick Look at Data Science: Forecasting Tourism Jobs in Canada 575

17.5.2 A Quick Look at Data Science: Forecasting Retail Gross Sales of Marijuana in Denver 577

Chapter Problems 580

Appendix A Math Notation and Symbols 583

A.1 Summation 583

A.2 pth Power 583

A.3 Inequalities 584

A.4 Factorials 584

A.5 Exponential Function 585

A.6 Greek and Statistics Symbols 585

Appendix B Standard Normal Cumulative Distribution Function 587

Appendix C t Distribution Critical Values 591

Appendix D Solutions to Odd-Numbered Problems 593

Index 643

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