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Data Science for Business and Decision Making,

Edition 1

Editors: By Luiz Paulo Fávero and Patrícia Belfiore

Publication Date: 22 Apr 2019

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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®.

Key Features

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

About the author

By Luiz Paulo Fávero, Economics, Business Administration and Accounting College of the University of Sao Paulo, Brazil/ Faculdade de Economia, Administracao e Contabilidade, Universidade de Sao Paulo, Brazil and Patrícia Belfiore, Federal University of ABC (UFABC)/ Federal University of ABC, Brazil

Part 1: Foundations of Business Data Analysis
1. Introduction to Data Analysis and Decision Making
2. Type of Variables and Mensuration Scales

Part 2: Descriptive Statistics
3. Univariate Descriptive Statistics
4. Bivariate Descriptive Statistics

Part 3: Probabilistic Statistics
5. Introduction of Probability
6. Random Variables and Probability Distributions

Part 4: Statistical Inference
7. Sampling
8. Estimation
9. Hypothesis Tests
10. Non-parametric Tests

Part 5: Multivariate Exploratory Data Analysis
11. Cluster Analysis
12. Principal Components Analysis and Factorial Analysis

Part 6: Generalized Linear Models
13. Simple and Multiple Regression Models
14. Binary and Multinomial Logistics Regression Models
15. Regression Models for Count Data: Poisson and Negative Binomial

Part 7: Optimization Models and Simulation
16. Introduction to Optimization Models: Business Problems Formulations and Modeling
17. Solution of Linear Programming Problems
18. Network Programming
19. Integer Programming
20. Simulation and Risk Analysis

Part 8: Other Topics
21. Design and Experimental Analysis
22. Statistical Process Control
23. Data Mining and Multilevel Modeling

Title Reviews

"Data Science for Business and Decision Making brings together the key topics required as the foundation for understanding and applying analytics for decision making. The authors have carefully selected the topics, and each one is clearly explained, described, and reinforced with a diverse set of exercises." —Rahul Saxena, Cobot Systems

"Data Science for Business and Decision Making provides a thorough essay about statistical methods which are commonly used in business without requiring a strong mathematical background. The presentation is rigorous and accessible thanks to a large number of examples that are developed step-by-step. The illustrations feature various software and the proposed exercises are particularly helpful for students and practitioners."—Francesco Bartolucci, University of Perugia

ISBN: 9780128112168

Page Count: 1244

Retail Price (USD) :

Christoffersen, Elements of Financial Risk Management, 2e, 2011, 9780123744487, 344pp, $69.95
Ross, Introduction to Probability Models, 11e, 9780124079489, 2014, 784pp, $99.95
Ross, Simulation, 5e, 9780124158252, 2012, 328pp, $99.95

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Upper-division undergraduates and graduate students worldwide working on business decision-making. This book will help them with statistics, particularly optimization and multivariate modeling, and their manipulation through the use of Excel, SPSS, and Stata

Data Science for Business and Decision Making,

Editors: By Luiz Paulo Fávero and Patrícia Belfiore

Key Features

About the author

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