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Data science for business and decision making

Author: Luiz Paulo Fávero; Patrícia Belfiore
Publisher: London, United Kingdom ; Cambridge, MA : Academic Press, an imprint of Elsevier, [2019]
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
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.  Read more...
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Genre/Form: Electronic books
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Luiz Paulo Fávero; Patrícia Belfiore
ISBN: 9780128112175 0128112174
OCLC Number: 1098238973
Description: 1 online resource.
Contents: 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
Responsibility: Luiz Paulo Fávero, Patrícia Belfiore.

Abstract:

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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"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 Read more...

 
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