Data science for business and decision making / Luiz Paulo Fávero, Patrícia Belfiore.

Format:

• Book

Author/Creator:

• Fávero, Luiz Paulo, author.
• Belfiore, Patrícia Prado, author.

Language:

English

Subjects (All):

• Decision making--Statistical methods.
• Commercial statistics.

Physical Description:

1 online resource (1 volume) : illustrations

Edition:

First edition.

Place of Publication:

London, United Kingdom ; San Diego, CA : Academic Press, [2019]

System Details:

text file

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. 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

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.

Notes:

• Includes bibliographical references and index.
• Description based on online resource; title from title page (Safari, viewed October 29, 2019).

ISBN:

• 9780128112175
• 0128112174
• 9780128112168
• 0128112166

OCLC:

1125343539