Principles of Managerial Statistics and Data Science

1. Auflage 2020
ISBN: 978-1-119-48641-1
Verlag: Wiley

Buch, Englisch, 688 Seiten, Format (B × H): 155 mm x 231 mm, Gewicht: 930 g

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.

Autoren/Hrsg.

Rivera, Roberto

Fachgebiete

• Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Datenanalyse, Datenverarbeitung
• Mathematik | Informatik Mathematik Mathematik Interdisziplinär Finanz- und Versicherungsmathematik
• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
• Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsmathematik und -statistik

Inhaltsverzeichnis

Preface xv

Acknowledgments xvii

Acronyms xix

About the Companion Site xxi

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

Further Reading 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

Further Reading 39

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

Further Reading 96

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

Further Reading 139

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

Further Reading 175

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

Further Reading 208

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

Über Autor(innen)

ROBERTO RIVERA, PHD, is a Professor, at the College of Business, University of Puerto Rico, Mayagüez. He received his PhD in Statistics from the University of California, Santa Barbara. He founded the Puerto Rico Chapter of the American Statistical Association. Dr. Rivera is also the co-author of Applications of Regression Models in Epidemiology (2017).