Statistics for Health Care Management and Administration

David A. Rosenthal is a Chief Information Officer at CampusWorks, Inc. He has over 20 years of health care industry experience in both academic and practitioner settings, having served in roles specific to hospital information technology leadership, multispecialty practice administration, and ambulatory services project management. Rosenthal earned a Master of Public Administration degree from Valdosta State University, and a PhD in Technology Management from Indiana State University. David is a proud veteran of the U.S. Air Force, and resides in Oakland, Tennessee with his wife Allyson. John F. Kros is the Vincent K. McMahon Distinguished Professor in the Marketing and Supply Chain Management Department in the College of Business at East Carolina University, in Greenville, North Carolina. He teaches business decision modeling, statistics, operations and supply chain management, and purchasing and materials management courses. Kros was honored as the College of Business’s Scholar/Teacher for 2004???2005, again in 2009???2010, was awarded the College of Business Commerce Club’s highest honor, the Teaching Excellence Award, for 2006 and again in 2011, in 2013???2014 was awarded the East Carolina Alumni Association Outstanding Teaching Award, and in 2014???2015 was awarded the Board of Governors Distinguished Professor for Teaching Award. Kros earned his PhD in systems engineering from the University of Virginia, his MBA from Santa Clara University, and his BBA from the University of Texas at Austin. His research interests include health care operations, applied statistics, design of experiments, multi-objective decision making, Taguchi methods, and applied decision analysis. His textbook titled Spreadsheet Modeling for Business Decisions is in the fifth edition. He is also coauthor of Health Care Operations and Supply Chain Management.

Preface xiii

Introducing Excel xiii

So How Did We Get to Here? xiii

Intended Level of the Textbook xiv

Textbook Organization xiv

Leading by Example(s) xv

Acknowledgments xvii

The Authors xix

About the Companion Website xxi

Part 1 1

Chapter 1 Statistics and Excel 3

1.1 How This Book Differs from Other Statistics Texts 3

1.2 Statistical Applications in Health Policy and Health Administration 4

Exercises for Section 1.2 14

1.3 What Is the ‘‘Big Picture’’? 15

1.4 Some Initial Definitions 16

Exercises for Section 1.4 26

1.5 Five Statistical Tests 28

Exercises for Section 1.5 30

Chapter 2 Excel as a Statistical Tool 33

2.1 The Basics 33

Exercises for Section 2.1 35

2.2 Working and Moving Around in a Spreadsheet 36

Exercises for Section 2.2 41

2.3 Excel Functions 41

Exercises for Section 2.3 46

2.4 The =IF() Function 47

Exercises for Section 2.4 50

2.5 Excel Graphs 51

Exercises for Section 2.5 56

2.6 Sorting a String of Data 57

Exercise for Section 2.6 60

2.7 The Data Analysis Pack 61

2.8 Functions That Give Results in More than One Cell 63

Exercises for Section 2.8 66

2.9 The Dollar Sign ($) Convention for Cell References 67

Chapter 3 Data Acquisition: Sampling and Data Preparation 71

3.1 The Nature of Data 71

Exercises for Section 3.1 78

3.2 Sampling 79

Exercises for Section 3.2 93

3.3 Data Access and Preparation 94

Exercises for Section 3.3 107

3.4 Missing Data 108

Chapter 4 Data Display: Descriptive Presentation, Excel Graphing Capability 111

4.1 Creating, Displaying, and Understanding Frequency Distributions 111

Exercises for Section 4.1 129

4.2 Using the Pivot Table to Generate Frequencies of Categorical Variables 131

Exercises for Section 4.2 135

4.3 A Logical Extension of the Pivot Table: Two Variables 135

Exercises for Section 4.3 140

Chapter 5 Basic Concepts of Probability 141

5.1 Some Initial Concepts and Definitions 141

Exercises for Section 5.1 150

5.2 Marginal Probabilities, Joint Probabilities, and Conditional Probabilities 150

Exercises for Section 5.2 160

5.3 Binomial Probability 161

Exercises for Section 5.3 171

5.4 The Poisson Distribution 173

Exercises for Section 5.4 178

5.5 The Normal Distribution 178

Chapter 6 Measures of Central Tendency and Dispersion: Data Distributions 183

6.1 Measures of Central Tendency and Dispersion 183

Exercises for Section 6.1 196

6.2 The Distribution of Frequencies 197

Exercises for Section 6.2 208

6.3 The Sampling Distribution of the Mean 209

Exercises for Section 6.3 219

6.4 Mean and Standard Deviation of a Discrete Numerical Variable 220

Exercises for Section 6.4 222

6.5 The Distribution of a Proportion 222

Exercises for Section 6.5 227

6.6 The t Distribution 227

Exercises for Section 6.6 232

Part 2 235

Chapter 7 Confidence Limits and Hypothesis Testing 237

7.1 What Is a Confidence Interval? 237

Exercises for Section 7.1 243

7.2 Calculating Confidence Limits for Multiple Samples 244

Exercises for Section 7.2 246

7.3 What Is Hypothesis Testing? 247

Exercises for Section 7.3 249

7.4 Type I and Type II Errors 250

Exercises for Section 7.4 266

7.5 Selecting Sample Sizes 267

Exercises for Section 7.5 269

Chapter 8 Statistical Tests for Categorical Data 271

8.1 Independence of Two Variables 271

Exercises for Section 8.1 282

8.2 Examples of Chi-Square Analyses 283

Exercises for Section 8.2 289

8.3 Small Expected Values in Cells 290

Exercises for Section 8.3 292

Chapter 9 t Tests for Related and Unrelated Data 295

9.1 What Is a t Test? 295

Exercises for Section 9.1 302

9.2 A t Test for Comparing Two Groups 303

Exercises for Section 9.2 316

9.3 A t Test for Related Data 318

Exercises for Section 9.3 321

Chapter 10 Analysis of Variance 323

10.1 One-Way Analysis of Variance 323

Exercises for Section 10.1 339

10.2 ANOVA for Repeated Measures 340

Exercises for Section 10.2 348

10.3 Factorial Analysis of Variance 349

Exercises for Section 10.3 362

Chapter 11 Simple Linear Regression 365

11.1 Meaning and Calculation of Linear Regression 365

Exercises for Section 11.1 373

11.2 Testing the Hypothesis of Independence 374

Exercises for Section 11.2 380

11.3 The Excel Regression Add-In 381

Exercises for Section 11.3 388

11.4 The Importance of Examining the Scatterplot 388

11.5 The Relationship between Regression and the t Test 391

Exercises for Section 11.5 392

Chapter 12 Multiple Regression: Concepts and Calculation 395

12.1 Introduction 395

Exercises for Section 12.1 406

Chapter 13 Extensions of Multiple Regression 409

13.1 Dummy Variables in Multiple Regression 409

Exercises for Section 13.1 420

13.2 The Best Regression Model 421

Exercises for Section 13.2 431

13.3 Correlation and Multicolinearity 432

Exercises for Section 13.3 435

13.4 Nonlinear Relationships 435

Exercises for Section 13.4 447

Chapter 14 Analysis with a Dichotomous Categorical Dependent Variable 449

14.1 Introduction to the Dichotomous Dependent Variable 450

14.2 An Example with a Dichotomous Dependent Variable: Traditional Treatments 451

Exercises for Section 14.2 462

14.3 Logit for Estimating Dichotomous Dependent Variables 463

Exercises for Section 14.3 475

14.4 A Comparison of Ordinary Least Squares, Weighted Least Squares, and Logit 476

Exercises for Section 14.4 480

Appendix A Multiple Regression and Matrices 481

An Introduction to Matrix Math 481

Addition and Subtraction of Matrices 482

Multiplication of Matrices 483

Matrix Multiplication and Scalars 484

Finding the Determinant of a Matrix 484

Matrix Capabilities of Excel 486

Explanation of Excel Output Displayed with Scientific Notation 489

Using the b Coefficients to Generate Regression Results 490

Calculation of All Multiple Regression Results 491

Exercises for Appendix A 494

References 497

Glossary 499

Index 513