Cover image for Data Science for Business and Decision Making

Data Science for Business and Decision Making

Author Patrícia Belfiore , Luiz Paulo Fávero
Release Date: 2019/04/01
ISBN: 9780128112175
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Book Description

Data Science for Business and Decision Making covers both statistics and operations research while most competing textbooks focus on one or the other. As a result, the book more clearly defines the principles of business analytics for those who want to apply quantitative methods in their work. Its emphasis reflects the importance of regression, optimization and simulation for practitioners of business analytics. Each chapter uses a didactic format that is followed by exercises and answers. Freely-accessible datasets enable students and professionals to work with Excel, Stata Statistical Software®, and IBM SPSS Statistics Software®.

  • Combines statistics and operations research modeling to teach the principles of business analytics
  • Written for students who want to apply statistics, optimization and multivariate modeling to gain competitive advantages in business
  • Shows how powerful software packages, such as SPSS and Stata, can create graphical and numerical outputs

Table of Contents

  1. Cover image
  2. Title page
  3. Table of Contents
  4. Copyright
  5. Dedication
  6. Epigraph
  7. Part I: Foundations of Business Data Analysis
    1. Chapter 1: Introduction to Data Analysis and Decision Making
      1. Abstract
      2. 1.1 Introduction: Hierarchy Between Data, Information, and Knowledge
      3. 1.2 Overview of the Book
      4. 1.3 Final Remarks
    2. Chapter 2: Types of Variables and Measurement and Accuracy Scales
      1. Abstract
      2. 2.1 Introduction
      3. 2.2 Types of Variables
      4. 2.3 Types of Variables × Scales of Measurement
      5. 2.4 Types of Variables × Number of Categories and Scales of Accuracy
      6. 2.5 Final Remarks
      7. 2.6 Exercises
  8. Part II: Descriptive Statistics
    1. Chapter 3: Univariate Descriptive Statistics
      1. Abstract
      2. 3.1 Introduction
      3. 3.2 Frequency Distribution Table
      4. 3.3 Graphical Representation of the Results
      5. 3.4 The Most Common Summary-Measures in Univariate Descriptive Statistics
      6. 3.5 A Practical Example in Excel
      7. 3.6 A Practical Example on SPSS
      8. 3.7 A Practical Example on Stata
      9. 3.8 Final Remarks
      10. 3.9 Exercises
    2. Chapter 4: Bivariate Descriptive Statistics
      1. Abstract
      2. 4.1 Introduction
      3. 4.2 Association Between Two Qualitative Variables
      4. 4.3 Correlation Between Two Quantitative Variables
      5. 4.4 Final Remarks
      6. 4.5 Exercises
  9. Part III: Probabilistic Statistics
    1. Chapter 5: Introduction to Probability
      1. Abstract
      2. 5.1 Introduction
      3. 5.2 Terminology and Concepts
      4. 5.3 Definition of Probability
      5. 5.4 Basic Probability Rules
      6. 5.5 Conditional Probability
      7. 5.6 Bayes' Theorem
      8. 5.7 Combinatorial Analysis
      9. 5.8 Final Remarks
      10. 5.9 Exercises
    2. Chapter 6: Random Variables and Probability Distributions
      1. Abstract
      2. 6.1 Introduction
      3. 6.2 Random Variables
      4. 6.3 Probability Distributions for Discrete Random Variables
      5. 6.4 Probability Distributions for Continuous Random Variables
      6. 6.5 Final Remarks
      7. 6.6 Exercises
  10. Part IV: Statistical Inference
    1. Chapter 7: Sampling
      1. Abstract
      2. 7.1 Introduction
      3. 7.2 Probability or Random Sampling
      4. 7.3 Nonprobability or Nonrandom Sampling
      5. 7.4 Sample Size
      6. 7.5 Final Remarks
      7. 7.6 Exercises
    2. Chapter 8: Estimation
      1. Abstract
      2. 8.1 Introduction
      3. 8.2 Point and Interval Estimation
      4. 8.3 Point Estimation Methods
      5. 8.4 Interval Estimation or Confidence Intervals
      6. 8.5 Final Remarks
      7. 8.6 Exercises
    3. Chapter 9: Hypotheses Tests
      1. Abstract
      2. 9.1 Introduction
      3. 9.2 Parametric Tests
      4. 9.3 Univariate Tests for Normality
      5. 9.4 Tests for the Homogeneity of Variances
      6. 9.5 Hypotheses Tests Regarding a Population Mean (μ) From One Random Sample
      7. 9.6 Student’s t-Test to Compare Two Population Means From Two Independent Random Samples
      8. 9.7 Student’s t-Test to Compare Two Population Means From Two Paired Random Samples
      9. 9.8 ANOVA to Compare the Means of More Than Two Populations
      10. 9.9 Final Remarks
      11. 9.10 Exercises
    4. Chapter 10: Nonparametric Tests
      1. Abstract
      2. 10.1 Introduction
      3. 10.2 Tests for One Sample
      4. 10.3 Tests for Two Paired Samples
      5. 10.4 Tests for Two Independent Samples
      6. 10.5 Tests for k Paired Samples
      7. 10.6 Tests for k Independent Samples
      8. 10.7 Final Remarks
      9. 10.8 Exercises
  11. Part V: Multivariate Exploratory Data Analysis
    1. Introduction
    2. Chapter 11: Cluster Analysis
      1. Abstract
      2. 11.1 Introduction
      3. 11.2 Cluster Analysis
      4. 11.3 Cluster Analysis with Hierarchical and Nonhierarchical Agglomeration Schedules in SPSS
      5. 11.4 Cluster Analysis With Hierarchical and Nonhierarchical Agglomeration Schedules in Stata
      6. 11.5 Final Remarks
      7. 11.6 Exercises
      8. Appendix
    3. Chapter 12: Principal Component Factor Analysis
      1. Abstract
      2. 12.1 Introduction
      3. 12.2 Principal Component Factor Analysis
      4. 12.3 Principal Component Factor Analysis in SPSS
      5. 12.4 Principal Component Factor Analysis in Stata
      6. 12.5 Final Remarks
      7. 12.6 Exercises
      8. Appendix: Cronbach’s Alpha
  12. Part VI: Generalized Linear Models
    1. Introduction
    2. Chapter 13: Simple and Multiple Regression Models
      1. Abstract
      2. 13.1 Introduction
      3. 13.2 Linear Regression Models
      4. 13.3 Presuppositions of Regression Models Estimated by OLS
      5. 13.4 Nonlinear Regression Models
      6. 13.5 Estimation of Regression Models in Stata
      7. 13.6 Estimation of Regression Models in SPSS
      8. 13.7 Final Remarks
      9. 13.8 Exercises
      10. Appendix: Quantile Regression Models
    3. Chapter 14: Binary and Multinomial Logistic Regression Models
      1. Abstract
      2. 14.1 Introduction
      3. 14.2 The Binary Logistic Regression Model
      4. 14.3 The Multinomial Logistic Regression Model
      5. 14.4 Estimation of Binary and Multinomial Logistic Regression Models in Stata
      6. 14.5 Estimation of Binary and Multinomial Logistic Regression Models in SPSS
      7. 14.6 Final Remarks
      8. 14.7 Exercises
      9. Appendix: Probit Regression Models
    4. Chapter 15: Regression Models for Count Data: Poisson and Negative Binomial
      1. Abstract
      2. 15.1 Introduction
      3. 15.2 The Poisson Regression Model
      4. 15.3 The Negative Binomial Regression Model
      5. 15.4 Estimating Regression Models for Count Data in Stata
      6. 15.5 Regression Model Estimation for Count Data in SPSS
      7. 15.6 Final Remarks
      8. 15.7 Exercises
      9. Appendix: Zero-Inflated Regression Models
  13. Part VII: Optimization Models and Simulation
    1. Chapter 16: Introduction to Optimization Models: General Formulations and Business Modeling
      1. Abstract
      2. 16.1 Introduction to Optimization Models
      3. 16.2 Introduction to Linear Programming Models
      4. 16.3 Mathematical Formulation of a General Linear Programming Model
      5. 16.4 Linear Programming Model in the Standard and Canonical Forms
      6. 16.5 Assumptions of the Linear Programming Model
      7. 16.6 Modeling Business Problems Using Linear Programming
      8. 16.7 Final Remarks
      9. 16.8 Exercises
    2. Chapter 17: Solution of Linear Programming Problems
      1. Abstract
      2. 17.1 Introduction
      3. 17.2 Graphical Solution of a Linear Programming Problem
      4. 17.3 Analytical Solution of a Linear Programming Problem in Which m &lt; < n
      5. 17.4 The Simplex Method
      6. 17.5 Solution by Using a Computer
      7. 17.6 Sensitivity Analysis
      8. 17.7 Exercises
    3. Chapter 18: Network Programming
      1. Abstract
      2. 18.1 Introduction
      3. 18.2 Terminology of Graphs and Networks
      4. 18.3 Classic Transportation Problem
      5. 18.4 Transhipment Problem
      6. 18.5 Job Assignment Problem
      7. 18.6 Shortest Path Problem
      8. 18.7 Maximum Flow Problem
      9. 18.8 Exercises
    4. Chapter 19: Integer Programming
      1. Abstract
      2. 19.1 Introduction
      3. 19.2 Mathematical Formulation of a General Model for Integer Programming and/or Binary and Linear Relaxation
      4. 19.3 The Knapsack Problem
      5. 19.4 The Capital Budgeting Problem as a Model of Binary Programming
      6. 19.5 The Traveling Salesman Problem
      7. 19.6 The Facility Location Problem
      8. 19.7 The Staff Scheduling Problem
      9. 19.8 Exercises
    5. Chapter 20: Simulation and Risk Analysis
      1. Abstract
      2. 20.1 Introduction to Simulation
      3. 20.2 The Monte Carlo Method
      4. 20.3 Monte Carlo Simulation in Excel
      5. 20.4 Final Remarks
      6. 20.5 Exercises
  14. Part VIII: Other Topics
    1. Chapter 21: Design and Analysis of Experiments
      1. Abstract
      2. 21.1 Introduction
      3. 21.2 Steps in the Design of Experiments
      4. 21.3 The Four Principles of Experimental Design
      5. 21.4 Types of Experimental Design
      6. 21.5 One-Way Analysis of Variance
      7. 21.6 Factorial ANOVA
      8. 21.7 Final Remarks
      9. 21.8 Exercises
    2. Chapter 22: Statistical Process Control
      1. Abstract
      2. 22.1 Introduction
      3. 22.2 Estimating the Process Mean and Variability
      4. 22.3 Control Charts for Variables
      5. 22.4 Control Charts for Attributes
      6. 22.5 Process Capability
      7. 22.6 Final Remarks
      8. 22.7 Exercises
    3. Chapter 23: Data Mining and Multilevel Modeling
      1. Abstract
      2. 23.1 Introduction to Data Mining
      3. 23.2 Multilevel Modeling
      4. 23.3 Nested Data Structures
      5. 23.4 Hierarchical Linear Models
      6. 23.5 Estimation of Hierarchical Linear Models in Stata
      7. 23.6 Estimation of Hierarchical Linear Models in SPSS
      8. 23.7 Final Remarks
      9. 23.8 Exercises
      10. Appendix
  15. Answers
    1. Answer Keys: Exercises: Chapter 2
    2. Answer Keys: Exercises: Chapter 3
    3. Answer Keys: Exercises: Chapter 4
    4. Answer Keys: Exercises: Chapter 5
    5. Answer Keys: Exercises: Chapter 6
    6. Answer Keys: Exercises: Chapter 7
    7. Answer Keys: Exercises: Chapter 8
    8. Answer Keys: Exercises: Chapter 9
    9. Answer Keys: Exercises: Chapter 10
    10. Answer Keys: Exercises: Chapter 11
    11. Answer Keys: Exercises: Chapter 12
    12. Answer Keys: Exercises: Chapter 13
    13. Answer Keys: Exercises: Chapter 14
    14. Answer Keys: Exercises: Chapter 15
    15. Answer Keys: Exercises: Chapter 16
    16. Answer Keys: Exercises: Chapter 17
    17. Answer Keys: Exercises: Chapter 18
    18. Answer Keys: Exercises: Chapter 19
    19. Answer Keys: Exercises: Chapter 20
    20. Answer Keys: Exercises: Chapter 21
    21. Answer Keys: Exercises: Chapter 22
    22. Answer Keys: Exercises: Chapter 23
  16. Appendices
  17. References
  18. Index
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