Principles of Managerial Statistics and Data Science

Principles of Managerial Statistics and Data Science

by Roberto Rivera
Released February 2020
Publisher(s): Wiley
ISBN: 9781119486411

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Book description

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.

Table of contents

  1. Cover
  2. Preface
  3. Acknowledgments
  4. Acronyms
  5. About the Companion Website
  6. Principles of Managerial Statistics and Data Science
    1. Project Objective
  7. 1 Statistics Suck; So Why Do I Need to Learn About It?
    1. 1.1 Introduction
    2. Practice Problems
    3. 1.2 Data‐Based Decision Making: Some Applications
    4. 1.3 Statistics Defined
    5. 1.4 Use of Technology and the New Buzzwords: Data Science, Data Analytics, and Big Data
    6. Chapter Problems
    7. Further Reading
  8. 2 Concepts in Statistics
    1. 2.1 Introduction
    2. Practice Problems
    3. 2.2 Type of Data
    4. Practice Problems
    5. 2.3 Four Important Notions in Statistics
    6. Practice Problems
    7. 2.4 Sampling Methods
    8. Practice Problems
    9. 2.5 Data Management
    10. 2.6 Proposing a Statistical Study
    11. Chapter Problems
    12. Further Reading
  9. 3 Data Visualization
    1. 3.1 Introduction
    2. 3.2 Visualization Methods for Categorical Variables
    3. Practice Problems
    4. 3.3 Visualization Methods for Numerical Variables
    5. Practice Problems
    6. 3.4 Visualizing Summaries of More than Two Variables Simultaneously
    7. Practice Problems
    8. 3.5 Novel Data Visualization
    9. Chapter Problems
    10. Further Reading
  10. 4 Descriptive Statistics
    1. 4.1 Introduction
    2. 4.2 Measures of Centrality
    3. Practice Problems
    4. 4.3 Measures of Dispersion
    5. Practice Problems
    6. 4.4 Percentiles
    7. Practice Problems
    8. 4.5 Measuring the Association Between Two Variables
    9. Practice Problems
    10. 4.6 Sample Proportion and Other Numerical Statistics
    11. 4.7 How to Use Descriptive Statistics
    12. Chapter Problems
    13. Further Reading
  11. 5 Introduction to Probability
    1. 5.1 Introduction
    2. 5.2 Preliminaries
    3. Practice Problems
    4. 5.3 The Probability of an Event
    5. Practice Problems
    6. 5.4 Rules and Properties of Probabilities
    7. Practice Problems
    8. 5.5 Conditional Probability and Independent Events
    9. Practice Problems
    10. 5.6 Empirical Probabilities
    11. Practice Problems
    12. 5.7 Counting Outcomes
    13. Practice Problems
    14. Chapter Problems
    15. Further Reading
  12. 6 Discrete Random Variables
    1. 6.1 Introduction
    2. 6.2 General Properties
    3. Practice Problems
    4. 6.3 Properties of Expected Value and Variance
    5. Practice Problems
    6. 6.4 Bernoulli and Binomial Random Variables
    7. Practice Problems
    8. 6.5 Poisson Distribution
    9. Practice Problems
    10. 6.6 Optional: Other Useful Probability Distributions
    11. Chapter Problems
    12. Further Reading
  13. 7 Continuous Random Variables
    1. 7.1 Introduction
    2. Practice Problems
    3. 7.2 The Uniform Probability Distribution
    4. Practice Problems
    5. 7.3 The Normal Distribution
    6. Practice Problems
    7. 7.4 Probabilities for Any Normally Distributed Random Variable
    8. Practice Problems
    9. 7.5 Approximating the Binomial Distribution
    10. Practice Problems
    11. 7.6 Exponential Distribution
    12. Practice Problems
    13. Chapter Problems
    14. Further Reading
  14. 8 Properties of Sample Statistics
    1. 8.1 Introduction
    2. 8.2 Expected Value and Standard Deviation of
    3. Practice Problems
    4. 8.3 Sampling Distribution of When Sample Comes From a Normal Distribution
    5. Practice Problems
    6. 8.4 Central Limit Theorem
    7. Practice Problems
    8. 8.5 Other Properties of Estimators
    9. Chapter Problems
    10. Further Reading
  15. 9 Interval Estimation for One Population Parameter
    1. 9.1 Introduction
    2. 9.2 Intuition of a Two‐Sided Confidence Interval
    3. 9.3 Confidence Interval for the Population Mean: Known
    4. Practice Problems
    5. 9.4 Determining Sample Size for a Confidence Interval for
    6. Practice Problems
    7. 9.5 Confidence Interval for the Population Mean: Unknown
    8. Practice Problems
    9. 9.6 Confidence Interval for
    10. Practice Problems
    11. 9.7 Determining Sample Size for Confidence Interval
    12. Practice Problems
    13. 9.8 Optional: Confidence Interval for
    14. Chapter Problems
    15. Further Reading
  16. 10 Hypothesis Testing for One Population
    1. 10.1 Introduction
    2. 10.2 Basics of Hypothesis Testing
    3. 10.3 Steps to Perform a Hypothesis Test
    4. Practice Problems
    5. 10.4 Inference on the Population Mean: Known Standard Deviation
    6. Practice Problems
    7. 10.5 Hypothesis Testing for the Mean ( Unknown)
    8. Practice Problems
    9. 10.6 Hypothesis Testing for the Population Proportion
    10. Practice Problems
    11. 10.7 Hypothesis Testing for the Population Variance
    12. 10.8 More on the p‐Value and Final Remarks
    13. Chapter Problems
    14. Further Reading
  17. 11 Statistical Inference to Compare Parameters from Two Populations
    1. 11.1 Introduction
    2. 11.2 Inference on Two Population Means
    3. 11.3 Inference on Two Population Means – Independent Samples, Variances Known
    4. Practice Problems
    5. 11.4 Inference on Two Population Means When Two Independent Samples are Used – Unknown Variances
    6. Practice Problems
    7. 11.5 Inference on Two Means Using Two Dependent Samples
    8. Practice Problems
    9. 11.6 Inference on Two Population Proportions
    10. Practice Problems
    11. Chapter Problems
    12. References
    13. Further Reading
  18. 12 Analysis of Variance (ANOVA)
    1. 12.1 Introduction
    2. Practice Problems
    3. 12.2 ANOVA for One Factor
    4. Practice Problems
    5. 12.3 Multiple Comparisons
    6. Practice Problems
    7. 12.4 Diagnostics of ANOVA Assumptions
    8. Practice Problems
    9. 12.5 ANOVA with Two Factors
    10. Practice Problems
    11. 12.6 Extensions to ANOVA
    12. Chapter Problems
    13. Further Reading
  19. 13 Simple Linear Regression
    1. 13.1 Introduction
    2. 13.2 Basics of Simple Linear Regression
    3. Practice Problems
    4. 13.3 Fitting the Simple Linear Regression Parameters
    5. Practice Problems
    6. 13.4 Inference for Simple Linear Regression
    7. Practice Problems
    8. 13.5 Estimating and Predicting the Response Variable
    9. Practice Problems
    10. 13.6 A Binary
    11. Practice Problems
    12. 13.7 Model Diagnostics (Residual Analysis)
    13. Practice Problems
    14. 13.8 What Correlation Doesn't Mean
    15. Chapter Problems
    16. Further Reading
  20. 14 Multiple Linear Regression
    1. 14.1 Introduction
    2. 14.2 The Multiple Linear Regression Model
    3. Practice Problems
    4. 14.3 Inference for Multiple Linear Regression
    5. Practice Problems
    6. 14.4 Multicollinearity and Other Modeling Aspects
    7. Practice Problems
    8. 14.5 Variability Around the Regression Line: Residuals and Intervals
    9. Practice Problems
    10. 14.6 Modifying Predictors
    11. Practice Problems
    12. 14.7 General Linear Model
    13. Practice Problems
    14. 14.8 Steps to Fit a Multiple Linear Regression Model
    15. 14.9 Other Regression Topics
    16. Chapter Problems
    17. Further Reading
  21. 15 Inference on Association of Categorical Variables
    1. 15.1 Introduction
    2. 15.2 Association Between Two Categorical Variables
    3. Practice Problems
    4. Chapter Problems
    5. Further Reading
  22. 16 Nonparametric Testing
    1. 16.1 Introduction
    2. 16.2 Sign Tests and Wilcoxon Sign‐Rank Tests: One Sample and Matched Pairs Scenarios
    3. Practice Problems
    4. 16.3 Wilcoxon Rank‐Sum Test: Two Independent Samples
    5. Practice Problems
    6. 16.4 Kruskal–Wallis Test: More Than Two Samples
    7. Practice Problems
    8. 16.5 Nonparametric Tests Versus Their Parametric Counterparts
    9. Chapter Problems
    10. Further Reading
  23. 17 Forecasting
    1. 17.1 Introduction
    2. 17.2 Time Series Components
    3. Practice Problems
    4. 17.3 Simple Forecasting Models
    5. Practice Problems
    6. 17.4 Forecasting When Data Has Trend, Seasonality
    7. Practice Problems
    8. 17.5 Assessing Forecasts
    9. Chapter Problems
    10. Further Reading
  24. Appendix A: Math Notation and Symbols
    1. A.1 Summation
    2. A.2 pth Power
    3. A.3 Inequalities
    4. A.4 Factorials
    5. A.5 Exponential Function
    6. A.6 Greek and Statistics Symbols
  25. Appendix B: Standard Normal Cumulative Distribution Function
  26. Appendix C: t Distribution Critical Values
  27. Appendix D: Solutions to Odd‐Numbered Problems
  28. Index
  29. End User License Agreement

Product information

  • Title: Principles of Managerial Statistics and Data Science
  • Author(s): Roberto Rivera
  • Release date: February 2020
  • Publisher(s): Wiley
  • ISBN: 9781119486411