Principles of Managerial Statistics and Data Science

Front Cover
John Wiley & Sons, Feb 5, 2020 - Mathematics - 688 pages

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.

 

What people are saying - Write a review

We haven't found any reviews in the usual places.

Contents

10
8
Concepts in Statistics
15
Contents
36
Guidelines for Assessment and Instruction in Statistics Education
54
4
86
Descriptive Statistics
97
Practice Problems
108
7
132
Practice Problems
357
Suicide Rates Among Asian Men
364
1
366
Practice Problems
370
References
378
4
387
Practice Problems
390
3
408

Introduction to Probability
141
15
146
1
164
Discrete Random Variables
177
Continuous Random Variables
209
5
227
Properties of Sample Statistics
243
1
252
2
262
Interval Estimation for One Population Parameter
269
Hypothesis Testing for One Population
297
16
319
Statistical Inference to Compare Parameters from
349
Simple Linear Regression
421
Multiple Linear Regression
473
Inference on Association of Categorical Variables
519
Contents
523
Affordability and Business Environment
525
Chapter Problems
532
Two Independent Samples
539
Practice Problems
546
Time Series Components
552
Simple Forecasting Models
558
Practice Problems
569
Index
643
Copyright

Other editions - View all

Common terms and phrases

About the author (2020)

ROBERTO RIVERA, PHD, is a Professor, at the College of Business, University of Puerto Rico, MayagŁez. 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).

Bibliographic information