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Data Science for Business and Decision Making de Luiz Paulo Favero

Data Science for Business and Decision Making

Autor Luiz Paulo Favero, Patricia Belfiore
en Limba Engleză Paperback – 2 iun 2019
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
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Specificații

ISBN-13: 9780128112168
ISBN-10: 0128112166
Pagini: 1244
Dimensiuni: 216 x 276 x 49 mm
Greutate: 3.38 kg
Editura: ELSEVIER SCIENCE

Public țintă

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.

Cuprins

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

Recenzii

"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