Principles of Managerial Statistics and Data Science

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.

List of contents

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

About the author

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).

Summary

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.

Product details

• Authors: R Rivera, Roberto Rivera, Roberto (Family Health International Rivera, Rivera Roberto
• Publisher: Wiley, John and Sons Ltd
• Languages: English
• Product format: Hardback
• Released: 29.02.2020
• EAN: 9781119486411
• ISBN: 978-1-119-48641-1
• No. of pages: 688
• Subjects: Natural sciences, medicine, IT, technology > Mathematics > Probability theory, stochastic theory, mathematical statistics; Statistik, Wirtschaftsmathematik, Datenanalyse, Statistics, Wirtschaftsstatistik, Business & management, data analysis, Wirtschaft u. Management, Statistics for Finance, Business & Economics, Finanz- u. Wirtschaftsstatistik, Wirtschaftsmathematik u. -statistik, Business Statistics & Math