- Cover image
- Title page
- Table of Contents
- Copyright
- Dedication
- Epigraph
- Part I: Foundations of Business Data Analysis
- Part II: Descriptive Statistics
- Chapter 3: Univariate Descriptive Statistics
- Abstract
- 3.1 Introduction
- 3.2 Frequency Distribution Table
- 3.3 Graphical Representation of the Results
- 3.4 The Most Common Summary-Measures in Univariate Descriptive Statistics
- 3.5 A Practical Example in Excel
- 3.6 A Practical Example on SPSS
- 3.7 A Practical Example on Stata
- 3.8 Final Remarks
- 3.9 Exercises
- Chapter 4: Bivariate Descriptive Statistics
- Chapter 3: Univariate Descriptive Statistics
- Part III: Probabilistic Statistics
- Part IV: Statistical Inference
- Chapter 7: Sampling
- Chapter 8: Estimation
- Chapter 9: Hypotheses Tests
- Abstract
- 9.1 Introduction
- 9.2 Parametric Tests
- 9.3 Univariate Tests for Normality
- 9.4 Tests for the Homogeneity of Variances
- 9.5 Hypotheses Tests Regarding a Population Mean (μ) From One Random Sample
- 9.6 Student’s t-Test to Compare Two Population Means From Two Independent Random Samples
- 9.7 Student’s t-Test to Compare Two Population Means From Two Paired Random Samples
- 9.8 ANOVA to Compare the Means of More Than Two Populations
- 9.9 Final Remarks
- 9.10 Exercises
- Chapter 10: Nonparametric Tests
- Part V: Multivariate Exploratory Data Analysis
- Part VI: Generalized Linear Models
- Introduction
- Chapter 13: Simple and Multiple Regression Models
- Abstract
- 13.1 Introduction
- 13.2 Linear Regression Models
- 13.3 Presuppositions of Regression Models Estimated by OLS
- 13.4 Nonlinear Regression Models
- 13.5 Estimation of Regression Models in Stata
- 13.6 Estimation of Regression Models in SPSS
- 13.7 Final Remarks
- 13.8 Exercises
- Appendix: Quantile Regression Models
- Chapter 14: Binary and Multinomial Logistic Regression Models
- Abstract
- 14.1 Introduction
- 14.2 The Binary Logistic Regression Model
- 14.3 The Multinomial Logistic Regression Model
- 14.4 Estimation of Binary and Multinomial Logistic Regression Models in Stata
- 14.5 Estimation of Binary and Multinomial Logistic Regression Models in SPSS
- 14.6 Final Remarks
- 14.7 Exercises
- Appendix: Probit Regression Models
- Chapter 15: Regression Models for Count Data: Poisson and Negative Binomial
- Part VII: Optimization Models and Simulation
- Chapter 16: Introduction to Optimization Models: General Formulations and Business Modeling
- Abstract
- 16.1 Introduction to Optimization Models
- 16.2 Introduction to Linear Programming Models
- 16.3 Mathematical Formulation of a General Linear Programming Model
- 16.4 Linear Programming Model in the Standard and Canonical Forms
- 16.5 Assumptions of the Linear Programming Model
- 16.6 Modeling Business Problems Using Linear Programming
- 16.7 Final Remarks
- 16.8 Exercises
- Chapter 17: Solution of Linear Programming Problems
- Chapter 18: Network Programming
- Chapter 19: Integer Programming
- Abstract
- 19.1 Introduction
- 19.2 Mathematical Formulation of a General Model for Integer Programming and/or Binary and Linear Relaxation
- 19.3 The Knapsack Problem
- 19.4 The Capital Budgeting Problem as a Model of Binary Programming
- 19.5 The Traveling Salesman Problem
- 19.6 The Facility Location Problem
- 19.7 The Staff Scheduling Problem
- 19.8 Exercises
- Chapter 20: Simulation and Risk Analysis
- Chapter 16: Introduction to Optimization Models: General Formulations and Business Modeling
- Part VIII: Other Topics
- Answers
- Answer Keys: Exercises: Chapter 2
- Answer Keys: Exercises: Chapter 3
- Answer Keys: Exercises: Chapter 4
- Answer Keys: Exercises: Chapter 5
- Answer Keys: Exercises: Chapter 6
- Answer Keys: Exercises: Chapter 7
- Answer Keys: Exercises: Chapter 8
- Answer Keys: Exercises: Chapter 9
- Answer Keys: Exercises: Chapter 10
- Answer Keys: Exercises: Chapter 11
- Answer Keys: Exercises: Chapter 12
- Answer Keys: Exercises: Chapter 13
- Answer Keys: Exercises: Chapter 14
- Answer Keys: Exercises: Chapter 15
- Answer Keys: Exercises: Chapter 16
- Answer Keys: Exercises: Chapter 17
- Answer Keys: Exercises: Chapter 18
- Answer Keys: Exercises: Chapter 19
- Answer Keys: Exercises: Chapter 20
- Answer Keys: Exercises: Chapter 21
- Answer Keys: Exercises: Chapter 22
- Answer Keys: Exercises: Chapter 23
- Appendices
- References
- Index
Data Science for Business and Decision Making
First Edition
Luiz Paulo Fávero
School of Economics, Business and Accounting, University of São Paulo, São Paulo, SP, Brazil
Patrícia Belfiore
Center of Engineering, Modeling and Applied Social Sciences, Management Engineering, Federal University of ABC, São Bernardo do Campo, SP, Brazil
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